youngfish42/Awesome-FL

# Federated Learning Resources [](https://github.com/youngfish42/Awesome-FL/stargazers) [](https://awesome.re) [](https://github.com/youngfish42/image-registration-resources/blob/master/LICENSE)  **Table of Contents** - [Papers](#papers) - [FL in top-tier journal](#fl-in-top-tier-journal) - FL in top-tier conference and journal by category - [AI](#fl-in-top-ai-conference-and-journal) [ML](#fl-in-top-ml-conference-and-journal) [DM](#fl-in-top-dm-conference-and-journal) [Secure](#fl-in-top-secure-conference-and-journal) [CV](#fl-in-top-cv-conference-and-journal) [NLP](#fl-in-top-nlp-conference-and-journal) [IR](#fl-in-top-ir-conference-and-journal) [DB](#fl-in-top-db-conference-and-journal) [Network](#fl-in-top-network-conference-and-journal) [System](#fl-in-top-system-conference-and-journal) [Others](#fl-in-top-conference-and-journal-other-fields) - [FL on Graph Data and Graph Neural Networks](#fl-on-graph-data-and-graph-neural-networks) [[dblp]](https://dblp.uni-trier.de/search?q=Federated%20graph%7Csubgraph%7Cgnn) - [FL on Tabular Data](#fl-on-tabular-data) [[dblp]](https://dblp.org/search?q=federate%20tree%7Cboost%7Cbagging%7Cgbdt%7Ctabular%7Cforest%7CXGBoost) - [Framework](#framework) - [Datasets](#datasets) - [Surveys](#surveys) - [Tutorials and Courses](#tutorials-and-courses) - Key Conferences/Workshops/Journals - [Workshops](#workshops) [Special Issues](#journal-special-issues) [Special Tracks](#conference-special-tracks) - [Update log](#update-log) - [Acknowledgments](#acknowledgments) - [Citation](#citation) We use another project to automatically track updates to FL papers, click on [FL-paper-update-tracker](https://github.com/youngfish42/FL-paper-update-tracker) if you need it. Please note that if this page does not display the full content, **please visit [the official homepage](https://youngfish42.github.io/Awesome-FL) for full information.** **More items will be added to the repository**. Please feel free to suggest other key resources by opening an [issue](https://github.com/youngfish42/Awesome-FL/issues) report, submitting a pull request, or dropping me an email @ ([im.young@foxmail.com](mailto:im.young@foxmail.com)). If you want to communicate with more friends in the field of federated learning, please join the QQ group [联邦学习交流群], the group number is 833638275. Enjoy reading! **Repository Update Notice** # papers **categories** - Artificial Intelligence (IJCAI, AAAI, AISTATS, ALT, AI) - Machine Learning (NeurIPS, ICML, ICLR, COLT, UAI, Machine Learning, JMLR, TPAMI) - Data Mining (KDD, WSDM) - Secure (S&P, CCS, USENIX Security, NDSS) - Computer Vision (ICCV, CVPR, ECCV, MM, IJCV) - Natural Language Processing (ACL, EMNLP, NAACL, COLING) - Information Retrieval (SIGIR) - Database (SIGMOD, ICDE, VLDB) - Network (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW) - System (OSDI, SOSP, ISCA, MLSys, EuroSys, TPDS, DAC, TOCS, TOS, TCAD, TC) - Others (ICSE, FOCS, STOC)

Events

| Venue | 2024-2020 | before 2020 | | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | [IJCAI](https://dblp.uni-trier.de/search?q=federate%20venue%3AIJCAI%3A) | [25](https://www.ijcai.org/proceedings/2025/), [24](https://www.ijcai.org/proceedings/2024/), [23](https://www.ijcai.org/proceedings/2023/), [22](https://www.ijcai.org/proceedings/2022/), [21](https://www.ijcai.org/proceedings/2021/), [20](https://www.ijcai.org/proceedings/2020/) | [19](https://www.ijcai.org/proceedings/2019/) | | [AAAI](https://dblp.uni-trier.de/search?q=federate%20venue%3AAAAI%3A) | [26](https://dblp.org/db/conf/aaai/aaai2026.html), [25](https://dblp.org/db/conf/aaai/aaai2025.html), [24](https://dblp.org/db/conf/aaai/aaai2024.html), [23](https://dblp.org/db/conf/aaai/aaai2023), [22](https://aaai.org/Conferences/AAAI-22/wp-content/uploads/2021/12/AAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf), [21](https://aaai.org/Conferences/AAAI-21/wp-content/uploads/2020/12/AAAI-21_Accepted-Paper-List.Main_.Technical.Track_.pdf), [20](https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf) | - | | [AISTATS](https://dblp.uni-trier.de/search?q=federate%20venue%3AAISTATS%3A) | [25](https://proceedings.mlr.press/v258/), [24](http://proceedings.mlr.press/v238/), [23](http://proceedings.mlr.press/v206/), [22](http://proceedings.mlr.press/v151/), [21](http://proceedings.mlr.press/v130/), [20](http://proceedings.mlr.press/v108/) | - | | [ALT](https://dblp.uni-trier.de/search?q=federate%20streamid%3Aconf%2Falt%3A) | 22 | - | | [AI](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fai%3A) (J) | 26, 25, 23 | - | | [NeurIPS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANeurIPS%3A) | [24](https://openreview.net/group?id=NeurIPS.cc/2024/Conference#tab-accept-oral), [23](https://openreview.net/group?id=NeurIPS.cc/2023/Conference#tab-accept-oral), [22](https://papers.nips.cc/paper_files/paper/2022), [21](https://papers.nips.cc/paper/2021), [20](https://papers.nips.cc/paper/2020) | [18](https://papers.nips.cc/paper/2018), [17](https://papers.nips.cc/paper/17) | | [ICML](https://dblp.uni-trier.de/search?q=federate%20venue%3AICML%3A) | [25](https://icml.cc/Conferences/2025/Schedule?type=Poster), [24](https://icml.cc/Conferences/2024/Schedule?type=Poster), [23](https://icml.cc/Conferences/2023/Schedule?type=Poster), [22](https://icml.cc/Conferences/2022/Schedule?type=Poster), [21](https://icml.cc/Conferences/2021/Schedule?type=Poster), [20](https://icml.cc/Conferences/2020/Schedule?type=Poster) | [19](https://icml.cc/Conferences/2019/Schedule?type=Poster) | | [ICLR](https://dblp.uni-trier.de/search?q=federate%20venue%3AICLR%3A) | [25](https://openreview.net/group?id=ICLR.cc/2025), [24](https://openreview.net/group?id=ICLR.cc/2024/Conference), [23](https://openreview.net/group?id=ICLR.cc/2023/Conference), [22](https://openreview.net/group?id=ICLR.cc/2022/Conference), [21](https://openreview.net/group?id=ICLR.cc/2021/Conference), [20](https://openreview.net/group?id=ICLR.cc/2020/Conference) | - | | [COLT](https://dblp.org/search?q=federated%20venue%3ACOLT%3A) | [23](https://proceedings.mlr.press/v195/) | - | | [UAI](https://dblp.org/search?q=federated%20venue%3AUAI%3A) | [25](https://www.auai.org/uai2025/accepted_papers), [24](https://www.auai.org/uai2024/accepted_papers), [23](https://www.auai.org/uai2023/accepted_papers), [22](https://www.auai.org/uai2022/accepted_papers), [21](https://www.auai.org/uai2021/accepted_papers) | - | | [Machine Learning](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fml%3A) (J) | 26, 25, 24, 23, 22 | - | | [JMLR](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fjmlr%3A) (J) | 25, 24, 23, 22 | - | | [TPAMI](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fpami%3A) (J) | 26, 25, 24, 23, 22 | - | | [KDD](https://dblp.uni-trier.de/search?q=federate%20venue%3AKDD%3A) | [26](https://dl.acm.org/doi/proceedings/10.1145/3770854), [25](https://dl.acm.org/doi/proceedings/10.1145/3690624), [24](https://dl.acm.org/doi/proceedings/10.1145/3637528), [23](https://dl.acm.org/doi/proceedings/10.1145/3580305), [22](https://kdd.org/kdd2022/paperRT.html), [21](https://kdd.org/kdd2021/accepted-papers/index), [20](https://www.kdd.org/kdd2020/accepted-papers) | | | [WSDM](https://dblp.uni-trier.de/search?q=federate%20venue%3AWSDM%3A) | [26](https://dl.acm.org/doi/proceedings/10.1145/3773966),[25](https://www.wsdm-conference.org/2025/accepted-papers/), [24](https://www.wsdm-conference.org/2024/accepted-papers/), [23](https://www.wsdm-conference.org/2023/program/accepted-papers), [22](https://www.wsdm-conference.org/2022/accepted-papers/), [21](https://www.wsdm-conference.org/2021/accepted-papers.php) | [19](https://www.wsdm-conference.org/2019/accepted-papers.php) | | [S&P](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsp%3A) | [25](https://sp2025.ieee-security.org/program-papers.html), [24](https://sp2024.ieee-security.org/program-papers.html), [23](https://sp2023.ieee-security.org/program-papers.html), [22](https://www.ieee-security.org/TC/SP2022/program-papers.html) | [19](https://www.ieee-security.org/TC/SP2019/program-papers.html) | | [CCS](https://dblp.uni-trier.de/search?q=federate%20venue%3ACCS%3A) | [25](https://dl.acm.org/doi/proceedings/10.1145/3719027), [24](https://dl.acm.org/doi/proceedings/10.1145/3658644), [23](https://dl.acm.org/doi/proceedings/10.1145/3576915), [22](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html), [21](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [19](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/) | [17](https://acmccs.github.io/papers/) | | [USENIX Security](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fuss%3A) | [25](https://www.usenix.org/conference/usenixsecurity25/technical-sessions), [24](https://www.usenix.org/conference/usenixsecurity24/technical-sessions), [23](https://www.usenix.org/conference/usenixsecurity23/technical-sessions), [22](https://www.usenix.org/conference/usenixsecurity22/technical-sessions), [20](https://www.usenix.org/conference/usenixsecurity20/technical-sessions) | - | | [NDSS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANDSS%3A) | [26](https://www.ndss-symposium.org/ndss2026/accepted-papers/), [25](https://www.ndss-symposium.org/ndss2025/accepted-papers/), [24](https://www.ndss-symposium.org/ndss2024/accepted-papers/), [23](https://www.ndss-symposium.org/ndss2023/accepted-papers/), [22](https://www.ndss-symposium.org/ndss2022/accepted-papers/), [21](https://www.ndss-symposium.org/ndss2021/accepted-papers/) | - | | [CVPR](https://dblp.uni-trier.de/search?q=federate%20venue%3ACVPR%3A) | [25](https://openaccess.thecvf.com/CVPR2025?day=all), [24](https://openaccess.thecvf.com/CVPR2024?day=all), [23](https://openaccess.thecvf.com/CVPR2023?day=all), [22](https://openaccess.thecvf.com/CVPR2022), [21](https://openaccess.thecvf.com/CVPR2021?day=all) | - | | [ICCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AICCV%3A) | [23](https://openaccess.thecvf.com/ICCV2023?day=all),[21](https://openaccess.thecvf.com/ICCV2021?day=all) | - | | [ECCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AECCV%3A) | [24](https://www.ecva.net/papers.php), [22](https://www.ecva.net/papers.php), [20](https://www.ecva.net/papers.php) | - | | [MM](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fmm%3A) | [25](https://dl.acm.org/doi/proceedings/10.1145/3746027), [24](https://dl.acm.org/doi/proceedings/10.1145/3664647), [23](https://dl.acm.org/doi/proceedings/10.1145/3581783), [22](https://dblp.uni-trier.de/db/conf/mm/mm2022.html), [21](https://2021.acmmm.org/main-track-list), [20](https://2020.acmmm.org/main-track-list.html) | - | | [IJCV](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fijcv%3A) (J) | 25, 24 | - | | [ACL](https://dblp.uni-trier.de/search?q=federate%20venue%3AACL%3A) | [25](https://aclanthology.org/events/acl-2025/), [24](https://aclanthology.org/events/acl-2024/), [23](https://aclanthology.org/events/acl-2023/), [22](https://aclanthology.org/events/acl-2022/), [21](https://aclanthology.org/events/acl-2021/) | [19](https://aclanthology.org/events/acl-2019/) | | [NAACL](https://dblp.uni-trier.de/search?q=federate%20venue%3ANAACL-HLT%3A) | [24](https://aclanthology.org/events/naacl-2024/), [22](https://aclanthology.org/events/naacl-2022/), [21](https://aclanthology.org/events/naacl-2021/) | - | | [EMNLP](https://dblp.uni-trier.de/search?q=federate%20venue%3AEMNLP%3A) | [25](https://aclanthology.org/events/emnlp-2025/), [24](https://aclanthology.org/events/emnlp-2024/), [23](https://aclanthology.org/events/emnlp-2023/), [22](https://aclanthology.org/events/emnlp-2022/), [21](https://aclanthology.org/events/emnlp-2021/), [20](https://aclanthology.org/events/emnlp-2020/) | - | | [COLING](https://dblp.uni-trier.de/search?q=federate%20venue%3ACOLING%3A) | [25](https://aclanthology.org/volumes/2025.coling-main/), [20](https://aclanthology.org/events/coling-2020/) | - | | [SIGIR](https://dblp.uni-trier.de/search?q=federate%20venue%3ASIGIR%3A) | [25](https://dl.acm.org/doi/proceedings/10.1145/3726302), [24](https://dl.acm.org/doi/proceedings/10.1145/3626772), [23](https://dl.acm.org/doi/proceedings/10.1145/3539618), [22](https://dl.acm.org/doi/proceedings/10.1145/3477495), [21](https://dl.acm.org/doi/proceedings/10.1145/3404835), [20](https://dl.acm.org/doi/proceedings/10.1145/3397271) | - | | [SIGMOD](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsigmod%3A) | [25](https://2025.sigmod.org/sigmod_papers.shtml), [24](https://2024.sigmod.org/), [23](https://2023.sigmod.org/sigmod_research_list.shtml), [22](https://2022.sigmod.org/sigmod_research_list.shtml), [21](https://2021.sigmod.org/sigmod_research_list.shtml) | - | | [ICDE](https://dblp.uni-trier.de/search?q=federate%20venue%3AICDE%3A) | [25](https://ieee-icde.org/2025/research-papers/), [24](https://icde2024.github.io/), [23](https://icde2023.ics.uci.edu/papers-research-track/), [22](https://icde2022.ieeecomputer.my/accepted-research-track/), [21](https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding) | - | | [VLDB](https://dblp.org/search?q=federated%20streamid%3Ajournals%2Fpvldb%3A) | [25](https://vldb.org/pvldb/volumes/18), [24](https://vldb.org/pvldb/volumes/17), [23](https://vldb.org/pvldb/volumes/17), [22](https://vldb.org/pvldb/vol16-volume-info/), [21](https://vldb.org/pvldb/vol15-volume-info/), [21](http://www.vldb.org/pvldb/vol14/), [20](http://vldb.org/pvldb/vol13-volume-info/) | - | | [SIGCOMM](https://dblp.uni-trier.de/search?q=federate%20venue%3ASIGCOMM%3A) | 25 | - | | [INFOCOM](https://dblp.uni-trier.de/search?q=federate%20venue%3AINFOCOM%3A) | [25](https://infocom2025.ieee-infocom.org/program/accepted-paper-list-main-conference), [24](https://infocom2024.ieee-infocom.org/program/accepted-paper-list-main-conference), [23](https://infocom2023.ieee-infocom.org/program/accepted-paper-list-main-conference), [22](https://infocom2022.ieee-infocom.org/program/accepted-paper-list-main-conference), [21](https://infocom2021.ieee-infocom.org/accepted-paper-list-main-conference.html), [20](https://infocom2020.ieee-infocom.org/accepted-paper-list-main-conference.html) | [19](https://infocom2019.ieee-infocom.org/accepted-paper-list-main-conference.html), 18 | | [MobiCom](https://dblp.uni-trier.de/search?q=federate%20venue%3AMobiCom%3A) | [25](https://www.sigmobile.org/mobicom/2025/accepted.html), [24](https://www.sigmobile.org/mobicom/2024/accepted.html), [23](https://www.sigmobile.org/mobicom/2023/accepted.html), [22](https://www.sigmobile.org/mobicom/2022/accepted.html), [21](https://www.sigmobile.org/mobicom/2021/accepted.html), [20](https://www.sigmobile.org/mobicom/2020/accepted.php) | | | [NSDI](https://dblp.uni-trier.de/search?q=federate%20venue%3ANSDI%3A) | [25](https://www.usenix.org/conference/nsdi25/technical-sessions), 23([1](https://www.usenix.org/conference/nsdi23/spring-accepted-papers), [2](https://www.usenix.org/conference/nsdi23/fall-accepted-papers)) | - | | [WWW](https://dblp.uni-trier.de/search?q=federate%20venue%3AWWW%3A) | [26](https://dl.acm.org/doi/proceedings/10.1145/3774904), [25](https://dl.acm.org/doi/proceedings/10.1145/3696410), [24](https://www2024.thewebconf.org/accepted/research-tracks/), [23](https://www2023.thewebconf.org/program/accepted-papers/), [22](https://www2022.thewebconf.org/accepted-papers/), [21](https://www2021.thewebconf.org/program/papers-program/links/index.html) | | | [OSDI](https://dblp.org/search?q=federated%20venue%3AOSDI%3A) | 21 | - | | [SOSP](https://dblp.org/search?q=federated%20venue%3ASOSP%3A) | 21 | - | | [ISCA](https://dblp.org/search?q=federated%20venue%3AISCA%3A) | [24](https://www.iscaconf.org/isca2024/program/) | - | | [MLSys](https://dblp.org/search?q=federated%20venue%3AMLSys%3A) | [25](https://proceedings.mlsys.org/paper_files/paper/2025), [24](https://proceedings.mlsys.org/paper_files/paper/2024), [23](https://proceedings.mlsys.org/paper_files/paper/2023), [22](https://proceedings.mlsys.org/paper_files/paper/2022), [20](https://proceedings.mlsys.org/paper_files/paper/2020) | [19](https://proceedings.mlsys.org/paper_files/paper/2019) | | [EuroSys](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Feurosys%3A) | [26](https://2026.eurosys.org/papers.html#papers), [25](https://2025.eurosys.org/accepted-papers.html), [24](https://2024.eurosys.org/accepted-papers.html), [23](https://2023.eurosys.org/accepted-papers.html), 22, 21, 20 | | | [TPDS](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Ftpds%3A) (J) | 26, 25, 24, 23, 22, 21, 20 | - | | [DAC](https://dblp.uni-trier.de/search?q=federate%20venue%3ADAC%3A) | 25, 24, 22, 21 | - | | [TOCS](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ftocs%3A) | - | - | | [TOS](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ftos%3A) | - | - | | [TCAD](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ftcad%3A) | 26, 25, 24, 23, 22, 21 | - | | [TC](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ftc%3A) | 26, 25, 24, 23, 22, 21 | - | | [ICSE](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Ficse%3A) | [25](https://conf.researchr.org/track/icse-2025/icse-2025-research-track), [23](https://conf.researchr.org/track/icse-2023/icse-2023-technical-track?#event-overview), 21 | - | | [FOCS](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Ffocs%3A) | - | - | | [STOC](https://dblp.uni-trier.de/search?q=federate%20streamid%3Aconf%2Fstoc%3A) | - | - |

**keywords** Statistics: :fire: code is available & stars >= 100 | :star: citation >= 50 | :mortar_board: Top-tier venue **`kg.`**: Knowledge Graph | **`data.`**: dataset  |   **`surv.`**: survey ## fl in top-tier journal Papers of federated learning in Nature(and its sub-journals), Cell, Science(and Science Advances) and PANS refers to [WOS](https://www.webofscience.com/wos/woscc/summary/ed3f4552-5450-4de7-bf2c-55d01e20d5de-4301299e/relevance/1) search engine.

fl in top-tier journal

|Title | Venue | Year | Materials| | ------------------------------------------------------------ | --------------------- | ---- | ------------------------------------------------------------ | | Towards compute-efficient Byzantine-robust federated learning with fully homomorphic encryption | Nat. Mach. Intell. | 2025 | [[PUB](https://www.nature.com/articles/s42256-025-01107-6)] [[PDF](https://arxiv.org/abs/2408.06197)] [[CODE](https://github.com/siyang-jiang/Lancelot)] | | Incentivizing inclusive contributions in model sharing markets | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-62959-5)] [[CODE](https://github.com/19dx/iPFL)] | | FedECA: federated external control arms for causal inference with time-to-event data in distributed settings | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-62525-z)] [[CODE](https://github.com/owkin/fedeca)] | | Privacy-preserving multicenter differential protein abundance analysis with FedProt | Nat. Comput. Sci. | 2025 | [[PUB](https://www.nature.com/articles/s43588-025-00832-7)] [[CODE](https://github.com/Freddsle/FedProt)] | | Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-60466-1)] [[CODE](https://github.com/mlcommons/medperf/tree/fets-challenge)] | | A fully open AI foundation model applied to chest radiography | Nature | 2025 | [[PUB](https://www.nature.com/articles/s41586-025-09079-8)] [[CODE](https://github.com/jlianglab/Ark)] | | Federated learning using a memristor compute-in-memory chip with in situ physical unclonable function and true random number generator | Nat. Electron. | 2025 | [[PUB](https://www.nature.com/articles/s41928-025-01390-6)] | | A framework reforming personalized Internet of Things by federated meta-learning | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-59217-z)] [[CODE](https://github.com/IntelligentSystemsLab/generic_and_open_learning_federator/)] | | Achieving flexible fairness metrics in federated medical imaging | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-58549-0)] [[CODE](https://zenodo.org/records/15203267)] | | Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-58055-3)] [[CODE](https://github.com/paridis-11/DynamicFL)] | | Data-driven federated learning in drug discovery with knowledge distillation | Nat. Mach. Intell. | 2025 | [[PUB](https://www.nature.com/articles/s42256-025-00991-2)] [[CODE](https://github.com/LhasaLimited/FLuID_POC)] | | Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-56510-9)] | | Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models | Nat. Commun. | 2025 | [[PUB](https://www.nature.com/articles/s41467-025-56412-w)] [[新闻](https://ic.pku.edu.cn/kxyj/kycg1/d2c084006150492c93ae3e6b0cb1d7df.htm)] | | MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-024-53431-x)] [[CODE](https://github.com/SICC-Group/MatSwarm)] | | Introducing edge intelligence to smart meters via federated split learning | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-024-53352-9)] [[新闻](https://www.ces.org.cn/html/report/24110829-1.htm)] | | An international study presenting a federated learning AI platform for pediatric brain tumors | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-024-51172-5)] [[CODE](https://github.com/edhlee/FLPedBrain)] | | PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data | Science Advances | 2024 | [[PUB](https://www.science.org/doi/10.1126/sciadv.adh8601)] [[CODE](https://github.com/JoshuaChou2018/PPML-Omics)] | | Federated learning is not a cure-all for data ethics | Nat. Mach. Intell.(Comment) | 2024 | [[PUB](https://www.nature.com/articles/s42256-024-00813-x)] | | Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-024-44946-4)] [[CODE](https://github.com/baofengguat/RFLM-project/)] | | Selective knowledge sharing for privacy-preserving federated distillation without a good teacher | Nat. Commun. | 2024 | [[PUB](https://www.nature.com/articles/s41467-023-44383-9)] [[PDF](https://arxiv.org/abs/2304.01731)] [[CODE](https://github.com/shaojiawei07/Selective-FD)] | | A federated learning system for precision oncology in Europe: DigiONE | Nat. Med. (Comment) | 2024 | [[PUB](https://www.nature.com/articles/s41591-023-02715-8)] | | Multi-client distributed blind quantum computation with the Qline architecture | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-43617-0)] [[PDF](https://arxiv.org/abs/2306.05195)] | | Device-independent quantum randomness–enhanced zero-knowledge proof | PNAS | 2023 | [[PUB](https://www.pnas.org/doi/10.1073/pnas.2205463120)] [[PDF](https://arxiv.org/abs/2111.06717)] [[新闻](https://www.nsfc.gov.cn/publish/portal0/tab448/info90817.htm)] | | Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-43883-y)] | | Advocating for neurodata privacy and neurotechnology regulation | Nat. Protoc. (Perspective) | 2023 | [[PUB](https://www.nature.com/articles/s41596-023-00873-0)] | | Federated benchmarking of medical artificial intelligence with MedPerf | Nat. Mach. Intell. | 2023 | [[PUB](https://www.nature.com/articles/s42256-023-00652-2)] [[PDF](https://arxiv.org/abs/2110.01406)] [[CODE](https://github.com/mlcommons/MedPerf)] | | Algorithmic fairness in artificial intelligence for medicine and healthcare | Nat. Biomed. Eng. (Perspective) | 2023 | [[PUB](https://www.nature.com/articles/s41551-023-01056-8)] [[PDF](https://arxiv.org/abs/2110.00603)] | | Differentially private knowledge transfer for federated learning | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-38794-x)] [[CODE](https://github.com/taoqi98/PrivateKT)] | | Decentralized federated learning through proxy model sharing | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-38569-4)] [[PDF](https://arxiv.org/abs/2111.11343)] [[CODE](https://github.com/layer6ai-labs/ProxyFL)] | | Federated machine learning in data-protection-compliant research | Nat. Mach. Intell.(Comment) | 2023 | [[PUB](https://www.nature.com/articles/s42256-022-00601-5)] | | Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer | Nat. Med. | 2023 | [[PUB](https://www.nature.com/articles/s41591-022-02155-w)] [[CODE](https://github.com/Substra/substra)] | | Federated learning enables big data for rare cancer boundary detection | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-33407-5)] [[PDF](https://arxiv.org/abs/2204.10836)] [[CODE](https://github.com/FETS-AI/Front-End)] | | Federated learning and Indigenous genomic data sovereignty | Nat. Mach. Intell. (Comment) | 2022 | [[PUB](https://www.nature.com/articles/s42256-022-00551-y)] | | Federated disentangled representation learning for unsupervised brain anomaly detection | Nat. Mach. Intell. | 2022 | [[PUB](https://www.nature.com/articles/s42256-022-00515-2)] [[PDF](https://doi.org/https://doi.org/10.21203/rs.3.rs-722389/v1)] [[CODE](https://doi.org/10.5281/zenodo.6604161)] | | Shifting machine learning for healthcare from development to deployment and from models to data | Nat. Biomed. Eng. (Review Article) | 2022 | [[PUB](https://www.nature.com/articles/s41551-022-00898-y)] | | A federated graph neural network framework for privacy-preserving personalization | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-30714-9)] [[CODE](https://github.com/wuch15/FedPerGNN)] [[解读](https://zhuanlan.zhihu.com/p/487383715)] | | Communication-efficient federated learning via knowledge distillation | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-29763-x)] [[PDF](https://arxiv.org/abs/2108.13323)] [[CODE](https://zenodo.org/record/6383473)] | | Lead federated neuromorphic learning for wireless edge artificial intelligence | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-32020-w)] [[CODE](https://github.com/GOGODD/FL-EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] | | A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data | Sci. Rep. | 2022 | [[PUB](https://www.nature.com/articles/s41598-022-12833-x)] | | Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence | Nat. Mach. Intell. | 2021 | [[PUB](https://www.nature.com/articles/s42256-021-00421-z)] [[PDF](https://arxiv.org/abs/2111.09461)] [[CODE](https://github.com/HUST-EIC-AI-LAB/UCADI)] | | Federated learning for predicting clinical outcomes in patients with COVID-19 | Nat. Med. | 2021 | [[PUB](https://www.nature.com/articles/s41591-021-01506-3)] [[CODE](https://www.nature.com/articles/s41591-021-01506-3#code-availability)] | | Adversarial interference and its mitigations in privacy-preserving collaborative machine learning | Nat. Mach. Intell.(Perspective) | 2021 | [[PUB](https://www.nature.com/articles/s42256-021-00390-3)] | | Swarm Learning for decentralized and confidential clinical machine learning :star: | Nature :mortar_board: | 2021 | [[PUB](https://www.nature.com/articles/s41586-021-03583-3)] [[CODE](https://github.com/HewlettPackard/swarm-learning)] [[SOFTWARE](https://myenterpriselicense.hpe.com)] [[解读](https://zhuanlan.zhihu.com/p/379434722)] | | End-to-end privacy preserving deep learning on multi-institutional medical imaging | Nat. Mach. Intell. | 2021 | [[PUB](https://www.nature.com/articles/s42256-021-00337-8)] [[CODE](https://doi.org/10.5281/zenodo.4545599)] [[解读](https://zhuanlan.zhihu.com/p/484801505)] | | Communication-efficient federated learning | PANS. | 2021 | [[PUB](https://www.pnas.org/doi/full/10.1073/pnas.2024789118)] [[CODE](https://code.ihub.org.cn/projects/4394/repository/revisions/master/show/PNAS)] | | Breaking medical data sharing boundaries by using synthesized radiographs | Science. Advances. | 2020 | [[PUB](https://www.science.org/doi/10.1126/sciadv.abb7973)] [[CODE](https://github.com/peterhan91/Thorax_GAN)] | | Secure, privacy-preserving and federated machine learning in medical imaging :star: | Nat. Mach. Intell.(Perspective) | 2020 | [[PUB](https://www.nature.com/articles/s42256-020-0186-1)] |

## fl in top ai conference and journal Federated Learning papers accepted by top AI(Artificial Intelligence) conference and journal, Including [IJCAI](https://dblp.org/db/conf/ijcai/index.html)(International Joint Conference on Artificial Intelligence), [AAAI](https://dblp.uni-trier.de/db/conf/aaai/index.html)(AAAI Conference on Artificial Intelligence), [AISTATS](https://dblp.uni-trier.de/db/conf/aistats/index.html)(Artificial Intelligence and Statistics), [ALT](https://dblp.org/db/conf/alt/index.html)(International Conference on Algorithmic Learning Theory), [AI](https://dblp.uni-trier.de/db/journals/ai/index.html)(Artificial Intelligence). - [IJCAI](https://dblp.uni-trier.de/search?q=federate%20venue%3AIJCAI%3A) [2025](https://www.ijcai.org/proceedings/2025/), [2024](https://www.ijcai.org/proceedings/2024/), [2023](https://www.ijcai.org/proceedings/2023/), [2022](https://www.ijcai.org/proceedings/2022/), [2021](https://www.ijcai.org/proceedings/2021/), [2020](https://www.ijcai.org/proceedings/2020/), [2019](https://www.ijcai.org/proceedings/2019/) - [AAAI](https://dblp.uni-trier.de/search?q=federate%20venue%3AAAAI%3A) [2026](https://dblp.org/db/conf/aaai/aaai2026.html), [2025](https://dblp.org/db/conf/aaai/aaai2025.html), [2024](https://dblp.org/db/conf/aaai/aaai2024.html), [2023](https://dblp.org/db/conf/aaai/aaai2023), [2022](https://aaai.org/Conferences/AAAI-22/wp-content/uploads/2021/12/AAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf), [2021](https://aaai.org/Conferences/AAAI-21/wp-content/uploads/2020/12/AAAI-21_Accepted-Paper-List.Main_.Technical.Track_.pdf), [2020](https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf) - [AISTATS](https://dblp.uni-trier.de/search?q=federate%20venue%3AAISTATS%3A) [2025](https://proceedings.mlr.press/v258/), [2024](http://proceedings.mlr.press/v238/), [2023](http://proceedings.mlr.press/v206/), [2022](http://proceedings.mlr.press/v151/), [2021](http://proceedings.mlr.press/v130/), [2020](http://proceedings.mlr.press/v108/) - [ALT](https://dblp.uni-trier.de/search?q=federate%20streamid%3Aconf%2Falt%3A) 2022 - [AI](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fai%3A) 2026, 2025, 2023

fl in top ai conference and journal

### 2026 #### AAAI - A Unified Self-Regulating Training Framework for Federated Deep Reinforcement Learning. [[PUB](https://doi.org/10.1609/aaai.v40i32.39946)] - Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach. [[PUB](https://doi.org/10.1609/aaai.v40i33.39991)] - BIQ: Bisection Interval Quantization for Communication-efficient Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i25.39259)] - Breaking Cross-View Associations: Byzantine Model Poisoning Attack against Vertical Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i48.42327)] - Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach. [[PUB](https://doi.org/10.1609/aaai.v40i17.38472)] - Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs. [[PUB](https://doi.org/10.1609/aaai.v40i28.39569)] - Causally-Aware Attribute Completion for Incomplete Federated Graph Clustering. [[PUB](https://doi.org/10.1609/aaai.v40i28.39547)] - Class-Aware Active Annotation in Federated Semi-Supervised Learning for Medical Image Classification. [[PUB](https://doi.org/10.1609/aaai.v40i32.39964)] - Communication-Efficient Heterogeneous Federated Learning with Sparse Prototypes in Resource-Constrained Environments. [[PUB](https://doi.org/10.1609/aaai.v40i27.39441)] - CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation. [[PUB](https://doi.org/10.1609/aaai.v40i29.39628)] - DA-DFGAS: Differentiable Federated Graph Neural Architecture Search with Distribution-Aware Attentive Aggregation. [[PUB](https://doi.org/10.1609/aaai.v40i28.39573)] - Data Heterogeneity and Forgotten Labels in Split Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i31.39794)] - Decoupling Shared and Personalized Knowledge: A Dual-Branch Federated Learning Framework for Multi-Domain with Non-IID Data. [[PUB](https://doi.org/10.1609/aaai.v40i29.39660)] - Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation. [[PUB](https://doi.org/10.1609/aaai.v40i34.40109)] - DoBlock: Blocking Malicious Association Propagation for Backdoor-Robust Federated Learning Under Domain Skew. [[PUB](https://doi.org/10.1609/aaai.v40i30.39778)] - Domain-Aware Suppression and Aggregation for Federated DG ReID. [[PUB](https://doi.org/10.1609/aaai.v40i14.38214)] - DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models. [[PUB](https://doi.org/10.1609/aaai.v40i15.38239)] - Enhanced Federated Deep Multi-View Clustering Under Uncertainty Scenario. [[PUB](https://doi.org/10.1609/aaai.v40i32.39891)] - Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection. [[PUB](https://doi.org/10.1609/aaai.v40i35.40206)] - EvoFMVC: Trusted Federated Multi-View Clustering with Evolutionary Fusion. [[PUB](https://doi.org/10.1609/aaai.v40i33.40057)] - Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences. [[PUB](https://doi.org/10.1609/aaai.v40i28.39557)] - FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models. [[PUB](https://doi.org/10.1609/aaai.v40i28.39549)] - FedALT: Federated Fine-Tuning Through Adaptive Local Training with Rest-of-World LoRA. [[PUB](https://doi.org/10.1609/aaai.v40i24.39054)] - FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification. [[PUB](https://doi.org/10.1609/aaai.v40i14.38124)] - FedAU2: Attribute Unlearning for User-Level Federated Recommender Systems with Adaptive and Robust Adversarial Training. [[PUB](https://doi.org/10.1609/aaai.v40i28.39500)] - FedBRICK: Structural Bias Aware Heterogeneous Foundation Model Federated Tuning. [[PUB](https://doi.org/10.1609/aaai.v40i34.40083)] - FedCD: Towards Consolidated Distillation for Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i28.39494)] - FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data. [[PUB](https://doi.org/10.1609/aaai.v40i25.39176)] - FedDNA: DNA Sequence Reconstruction via Deep Evidential Learning and Personalized Federated Aggregation. [[PUB](https://doi.org/10.1609/aaai.v40i28.39524)] - Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification. [[PUB](https://doi.org/10.1609/aaai.v40i32.39912)] - Federated Context-Aware Personalized Recommendation. [[PUB](https://doi.org/10.1609/aaai.v40i31.39888)] - Federated Graph-level Clustering Network with Attribute Inference. [[PUB](https://doi.org/10.1609/aaai.v40i26.39307)] - Federated Incomplete Multi-View Clustering with Tensorized Low-Rank Constraint. [[PUB](https://doi.org/10.1609/aaai.v40i25.39251)] - Federated Learning Playground. [[PUB](https://doi.org/10.1609/aaai.v40i48.42349)] - Federated Linear Dueling Bandits. [[PUB](https://doi.org/10.1609/aaai.v40i26.39361)] - Federated Vision-Language-Recommendation with Personalized Fusion. [[PUB](https://doi.org/10.1609/aaai.v40i28.39503)] - FedLAGC: Towards High Performance System-Heterogeneous Federated Learning via Layer-Adaptive Submodel Extraction and Gradient Correction. [[PUB](https://doi.org/10.1609/aaai.v40i26.39338)] - FedMerge: Federated Model Merging for Personalization. [[PUB](https://doi.org/10.1609/aaai.v40i24.39113)] - FedPKDA: Personalized Federated Learning with Privacy-Preserving Knowledge Dynamic Alignment. [[PUB](https://doi.org/10.1609/aaai.v40i33.40037)] - FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters. [[PUB](https://doi.org/10.1609/aaai.v40i26.39368)] - FedP²EFT: Federated Learning to Personalize PEFT for Multilingual LLMs. [[PUB](https://doi.org/10.1609/aaai.v40i27.39443)] - FedRNC: Addressing Spatio-Temporal Label Misalignment in Federated Noisy Class-Incremental Learning. [[PUB](https://doi.org/10.1609/aaai.v40i26.39359)] - FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification. [[PUB](https://doi.org/10.1609/aaai.v40i12.37918)] - FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD. [[PUB](https://doi.org/10.1609/aaai.v40i26.39364)] - FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i34.40100)] - FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness. [[PUB](https://doi.org/10.1609/aaai.v40i32.39895)] - FedSkeleton: Secure Multi-Party Graph Skeleton Construction for Privacy-Preserving Federated Time-Series Forecasting. [[PUB](https://doi.org/10.1609/aaai.v40i25.39210)] - FedTopo: Topology-Informed Representation Alignment in Federated Learning Under Non-I.I.D. Conditions. [[PUB](https://doi.org/10.1609/aaai.v40i26.39337)] - FILTER: A Framework for Defending Against Backdoor Attacks in Vertical Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40859)] - Generalizable Heterogeneity-aware Federated Feature and Basic-matrix Consistency Learning. [[PUB](https://doi.org/10.1609/aaai.v40i27.39436)] - Generic Adversarial Attack Framework Against Graph-based Vertical Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40878)] - Good Gradients Poison Your Model: Evading Defenses in Federated Learning via Boundary-adaptive Perturbation. [[PUB](https://doi.org/10.1609/aaai.v40i16.38328)] - HealSplit: Towards Self-Healing Through Adversarial Distillation in Split Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40908)] - Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants. [[PUB](https://doi.org/10.1609/aaai.v40i24.39116)] - Incomplete Multi-View Unsupervised Federated Feature Selection via Cooperative Particle Swarm Optimization and Tensor-Aligned Learning. [[PUB](https://doi.org/10.1609/aaai.v40i33.40005)] - Inter-Client Dependency Recovery with Hidden Global Components for Federated Traffic Prediction. [[PUB](https://doi.org/10.1609/aaai.v40i34.40130)] - Intra-Class Unbiased Prototype Aggregation and Classifier Collaboration for Personalized Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i34.40113)] - Investigating Social Bias Propagation in Federated Fine-tuning of Large Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i46.41316)] - LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping. [[PUB](https://doi.org/10.1609/aaai.v40i25.39184)] - MSCFL: Model Structure-Aware Clustered Federated Learning for System Heterogeneity and Data Drift. [[PUB](https://doi.org/10.1609/aaai.v40i32.39952)] - Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization. [[PUB](https://doi.org/10.1609/aaai.v40i25.39177)] - MultiKD: Backdoor Defense in Federated Graph Learning via Attention-Guided Multi-Teacher Distillation. [[PUB](https://doi.org/10.1609/aaai.v40i33.40051)] - Neuro-Symbolic Federated Learning over Heterogeneous Data-Views: A Structured Approach to Distributive EHR Modelling. [[PUB](https://doi.org/10.1609/aaai.v40i29.39624)] - Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i33.40045)] - Optimal Look-back Horizon for Time Series Forecasting in Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i30.39781)] - OPTION: An Online Pricing Strategy for Asynchronous Federated Learning Against Free-Riding Attacks. [[PUB](https://doi.org/10.1609/aaai.v40i29.39653)] - OursFed: Provable Group Fairness-Aware Federated Learning Against Distrust and Fragility. [[PUB](https://doi.org/10.1609/aaai.v40i32.39926)] - PAGE: A Unified Approach for Federated Graph Unlearning. [[PUB](https://doi.org/10.1609/aaai.v40i24.39038)] - Personalized Federated Graph-Level Clustering Network. [[PUB](https://doi.org/10.1609/aaai.v40i28.39546)] - Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching. [[PUB](https://doi.org/10.1609/aaai.v40i25.39185)] - Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendation. [[PUB](https://doi.org/10.1609/aaai.v40i19.38660)] - Poisoning with a Pill: Circumventing Detection in Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i26.39290)] - PPFL: A Parameter Behavior-Driven Plug-in Personalization Engine for Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i24.39073)] - Prior Refinement Is Better: Diffusion-Driven Graph Harmonization for Federated Graph Learning. [[PUB](https://doi.org/10.1609/aaai.v40i34.40163)] - Re-architecting Personalized Federated Learning for Demanding Edge Environments. [[PUB](https://doi.org/10.1609/aaai.v40i29.39655)] - REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Feature. [[PUB](https://doi.org/10.1609/aaai.v40i32.39911)] - Retaliatory Attacks Against Federated Unlearning via Data Leakage. [[PUB](https://doi.org/10.1609/aaai.v40i30.39725)] - Ripple Shapley: Data Influence Attribution in One Federated Training Run. [[PUB](https://doi.org/10.1609/aaai.v40i33.40034)] - Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?. [[PUB](https://doi.org/10.1609/aaai.v40i24.39122)] - SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i31.39787)] - ShadeEdit: A Utility-Preserving and Defense-Evasive Knowledge Manipulation Attack in Federated LLMs. [[PUB](https://doi.org/10.1609/aaai.v40i41.40787)] - SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data. [[PUB](https://doi.org/10.1609/aaai.v40i32.39977)] - Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack. [[PUB](https://doi.org/10.1609/aaai.v40i32.39979)] - TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i33.40058)] - Topological Federated Clustering via Gravitational Potential Fields Under Local Differential Privacy. [[PUB](https://doi.org/10.1609/aaai.v40i28.39582)] - Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework. [[PUB](https://doi.org/10.1609/aaai.v40i26.39311)] - Towards Robust Text-Attributed Federated Graph Learning: Multimodal Threats and Defense. [[PUB](https://doi.org/10.1609/aaai.v40i30.39732)] - TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i33.40048)] - Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-temporal Graph Learning Method for Traffic Flow Forecasting. [[PUB](https://doi.org/10.1609/aaai.v40i2.37083)] - Venom: Liquid Diffusion-Guided Gradient Inversion for Breaking Differential Privacy in Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v40i26.39333)] - AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract). [[PUB](https://doi.org/10.1609/aaai.v40i48.42197)] - Federated Cross-Modal Style-Aware Prompt Generation (Student Abstract). [[PUB](https://doi.org/10.1609/aaai.v40i48.42268)] - UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data (Student Abstract). [[PUB](https://doi.org/10.1609/aaai.v40i48.42220)] - A Dialogue-Based Learning Analytics Framework for Collaborative Game-Based Learning. [[PUB](https://doi.org/10.1609/aaai.v40i48.42116)] - Advancing Protein Design via Multi-Agent Reinforcement Learning with Pareto-Based Collaborative Optimization. [[PUB](https://doi.org/10.1609/aaai.v40i2.37142)] - CL-Guard: Defending DNNs Against Backdoors via Fine-Grained Neuron Analysis and Collaborative Dual-Network Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40904)] - Collaborative Dual Representations for Semi-Supervised Partial Label Learning. [[PUB](https://doi.org/10.1609/aaai.v40i24.39049)] - Collaborative Feature Matching with Progressive Correspondence Learning. [[PUB](https://doi.org/10.1609/aaai.v40i9.37669)] - Collaborative Representation Learning for Alignment of Tactile, Language, and Vision Modalities. [[PUB](https://doi.org/10.1609/aaai.v40i22.38956)] - Cross-Domain Few-Shot Learning via Multi-View Collaborative Optimization with Vision-Language Models. [[PUB](https://doi.org/10.1609/aaai.v40i24.39086)] - DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning. [[PUB](https://doi.org/10.1609/aaai.v40i28.39561)] - Do Not Merge My Model! Safeguarding Open-Source LLMs Against Unauthorized Model Merging. [[PUB](https://doi.org/10.1609/aaai.v40i37.40433)] - Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning. [[PUB](https://doi.org/10.1609/aaai.v40i19.38656)] - From Parameter to Representation: A Closed-Form Approach for Controllable Model Merging. [[PUB](https://doi.org/10.1609/aaai.v40i32.39902)] - GLOBA: Rethinking Parameter Conflicts in Model Merging. [[PUB](https://doi.org/10.1609/aaai.v40i28.39572)] - Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation. [[PUB](https://doi.org/10.1609/aaai.v40i30.39742)] - Learning to Deliberate: Meta-policy Collaboration for Agentic LLMs with Multi-agent Reinforcement Learning. [[PUB](https://doi.org/10.1609/aaai.v40i35.40228)] - Learning to Generate and Extract: A Multi-Agent Collaboration Framework for Zero-Shot Document-Level Event Arguments Extraction. [[PUB](https://doi.org/10.1609/aaai.v40i41.40767)] - LLM Collaboration with Multi-Agent Reinforcement Learning. [[PUB](https://doi.org/10.1609/aaai.v40i38.40487)] - M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data. [[PUB](https://doi.org/10.1609/aaai.v40i31.39854)] - MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation. [[PUB](https://doi.org/10.1609/aaai.v40i14.38161)] - MergeDNA: Context-Aware Genome Modeling with Dynamic Tokenization Through Token Merging. [[PUB](https://doi.org/10.1609/aaai.v40i1.37032)] - Multi-view Invariance Learning for 3D Scene Graph Pre-training via Collaborative Cross-Modal Regularization. [[PUB](https://doi.org/10.1609/aaai.v40i7.37435)] - Outlier Matters: Efficient Long-to-Short Reasoning via Outlier-Guided Model Merging. [[PUB](https://doi.org/10.1609/aaai.v40i41.40828)] - RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior. [[PUB](https://doi.org/10.1609/aaai.v40i40.40722)] - Rep Deep & Machine Learning: Exemplar-Free Continual Video Action Recognition via Slow-Fast Collaborative Learning. [[PUB](https://doi.org/10.1609/aaai.v40i42.40924)] - Revisiting Contrastive Learning in Collaborative Filtering via Parallel Graph Filters. [[PUB](https://doi.org/10.1609/aaai.v40i17.38521)] - Think Wise, Collaborate Effectively: A Rationale-Aware LLM-Based Recommender with Reinforcement Learning from Collaborative Signals. [[PUB](https://doi.org/10.1609/aaai.v40i18.38590)] - Unifying Multi-View Knowledge for Graph Learning via Model Collaboration. [[PUB](https://doi.org/10.1609/aaai.v40i32.39914)] - Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent (Abstract Reprint). [[PUB](https://doi.org/10.1609/aaai.v40i47.41386)] #### AI - Disentangling data distribution for optimal and communication-efficient federated learning. [[PUB](https://doi.org/10.1016/j.artint.2025.104455)] - Federated neural nonparametric point processes. [[PUB](https://doi.org/10.1016/j.artint.2025.104454)] ### 2025 #### IJCAI - Exploiting Label Skewness for Spiking Neural Networks in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/767)] - FedHAN: A Cache-Based Semi-Asynchronous Federated Learning Framework Defending Against Poisoning Attacks in Heterogeneous Clients. [[PUB](https://www.ijcai.org/proceedings/2025/379)] - Heterogeneous Federated Learning with Scalable Server Mixture-of-Experts. [[PUB](https://www.ijcai.org/proceedings/2025/610)] - Pixel-wise Divide and Conquer for Federated Vessel Segmentation. [[PUB](https://www.ijcai.org/proceedings/2025/540)] - Universal Backdoor Defense via Label Consistency in Vertical Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/528)] - Where Does This Data Come From? Enhanced Source Inference Attacks in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/536)] - Optimizing Personalized Federated Learning Through Adaptive Layer-Wise Learning. [[PUB](https://www.ijcai.org/proceedings/2025/541)] [[CODE](https://github.com/lancasterJie/FLAYER)] - FedDLAD: A Federated Learning Dual-Layer Anomaly Detection Framework for Enhancing Resilience Against Backdoor Attacks. [[PUB](https://www.ijcai.org/proceedings/2025/559)] [[CODE](https://github.com/dingbinb/FedDLAD)] - Federated Multi-view Graph Clustering with Incomplete Attribute Imputation. [[PUB](https://www.ijcai.org/proceedings/2025/570)] - ADPFedGNN: Adaptive Decoupling Personalized Federated Graph Neural Network. [[PUB](https://www.ijcai.org/proceedings/2025/585)] - Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning. [[PUB](https://www.ijcai.org/proceedings/2025/590)] - FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition. [[PUB](https://www.ijcai.org/proceedings/2025/597)] - FedBG: Proactively Mitigating Bias in Cross-Domain Graph Federated Learning Using Background Data. [[PUB](https://www.ijcai.org/proceedings/2025/602)] - FedCCH: Automatic Personalized Graph Federated Learning for Inter-Client and Intra-Client Heterogeneity. [[PUB](https://www.ijcai.org/proceedings/2025/333)] - FedCPD:Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation. [[PUB](https://www.ijcai.org/proceedings/2025/612)] - Data Poisoning Attack Defense and Evolutionary Domain Adaptation for Federated Medical Image Segmentation. [[PUB](https://www.ijcai.org/proceedings/2025/146)] - Distilling A Universal Expert from Clustered Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/620)] - CSAHFL:Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks. [[PUB](https://www.ijcai.org/proceedings/2025/621)] - FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/628)] - Hypernetwork Aggregation for Decentralized Personalized Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/161)] - Federated Domain Generalization with Decision Insight Matrix. [[PUB](https://www.ijcai.org/proceedings/2025/633)] - Generic Adversarial Attack Framework Against Vertical Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/646)] - One-shot Federated Learning Methods: A Practical Guide. [[PUB](https://www.ijcai.org/proceedings/2025/1174)] - Federated Learning at the Forefront of Fairness: A Multifaceted Perspective. [[PUB](https://www.ijcai.org/proceedings/2025/1177)] - Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach. [[PUB](https://www.ijcai.org/proceedings/2025/670)] - Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization. [[PUB](https://www.ijcai.org/proceedings/2025/677)] - FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data. [[PUB](https://www.ijcai.org/proceedings/2025/692)] [[CODE](https://github.com/Yuxia-Sun/FL_FedAPA)] - An Empirical Study of Federated Prompt Learning for Vision Language Model. [[PUB](https://www.ijcai.org/proceedings/2025/1188)] - FedCM: Client Clustering and Migration in Federated Learning via Gradient Path Similarity and Update Direction Deviation. [[PUB](https://www.ijcai.org/proceedings/2025/706)] - Zero-shot Federated Unlearning via Transforming from Data-Dependent to Personalized Model-Centric. [[PUB](https://www.ijcai.org/proceedings/2025/733)] - DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning Under Two-sided Incomplete Information. [[PUB](https://www.ijcai.org/proceedings/2025/744)] - Backdoor Attack on Vertical Federated Graph Neural Network Learning. [[PUB](https://www.ijcai.org/proceedings/2025/877)] - Federated Low-Rank Adaptation for Foundation Models: A Survey. [[PUB](https://www.ijcai.org/proceedings/2025/1196)] - Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/761)] - FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization. [[PUB](https://www.ijcai.org/proceedings/2025/770)] - A Multi-Granularity Clustering Approach for Federated Backdoor Defense with the Adam Optimizer. [[PUB](https://www.ijcai.org/proceedings/2025/771)] - Federated Stochastic Bilevel Optimization with Fully First-Order Gradients. [[PUB](https://www.ijcai.org/proceedings/2025/784)] - AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling. [[PUB](https://www.ijcai.org/proceedings/2025/787)] - Rethinking Federated Graph Learning: A Data Condensation Perspective. [[PUB](https://www.ijcai.org/proceedings/2025/775)] - MMGIA: Gradient Inversion Attack Against Multimodal Federated Learning via Intermodal Correlation. [[PUB](https://www.ijcai.org/proceedings/2025/886)] - Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2025/798)] - Finite-Time Analysis of Heterogeneous Federated Temporal Difference Learning. [[PUB](https://www.ijcai.org/proceedings/2025/808)] - Inconsistency-Based Federated Active Learning. [[PUB](https://www.ijcai.org/proceedings/2025/812)] - CSAHFL: Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks. [[PUB](https://doi.org/10.24963/ijcai.2025/621)] - FedCPD: Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation. [[PUB](https://doi.org/10.24963/ijcai.2025/612)] - Bidirectional Human-AI Collaboration for Equitable Student Performance Prediction via Deep Uncertainty Learning. [[PUB](https://doi.org/10.24963/ijcai.2025/1114)] - Credit Assignment and Fine-Tuning Enhanced Reinforcement Learning for Collaborative Spatial Crowdsourcing. [[PUB](https://doi.org/10.24963/ijcai.2025/459)] - Cross-modal Collaborative Representation Learning for Text-to-Image Person Retrieval. [[PUB](https://doi.org/10.24963/ijcai.2025/240)] - Enhancing Mixture of Experts with Independent and Collaborative Learning for Long-Tail Visual Recognition. [[PUB](https://doi.org/10.24963/ijcai.2025/93)] [[CODE](https://github.com/PolarisLight/ICL)] #### AISTATS - Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation. [[PUB](https://proceedings.mlr.press/v258/thakur25a.html)] [[CODE](https://github.com/AnshThakur/FedMode)] - FedBaF: Federated Learning Aggregation Biased by a Foundation Model. [[PUB](https://proceedings.mlr.press/v258/park25b.html)] - Global Group Fairness in Federated Learning via Function Tracking. [[PUB](https://proceedings.mlr.press/v258/rychener25a.html)] [[CODE](https://github.com/yvesrychener/Fair-FL)] - On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond. [[PUB](https://proceedings.mlr.press/v258/zeng25b.html)] [[CODE](https://github.com/dunzeng/FedAWARE)] - Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. [[PUB](https://proceedings.mlr.press/v258/labbi25a.html)] [[CODE](https://github.com/Labbi-Safwan/Fed-UCBVI)] - ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning. [[PUB](https://proceedings.mlr.press/v258/ozkara25a.html)] [[CODE](https://github.com/kazkara/adept)] - Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis. [[PUB](https://proceedings.mlr.press/v258/khellaf25a.html)] [[CODE](https://github.com/RemiKhellaf/FedCausal-RCTs-Khellaf/)] - The cost of local and global fairness in Federated Learning. [[PUB](https://proceedings.mlr.press/v258/duan25a.html)] [[CODE](https://github.com/papersubmission678/The-cost-of-local-and-global-fairness-in-FL)] - Federated Communication-Efficient Multi-Objective Optimization. [[PUB](https://proceedings.mlr.press/v258/askin25a.html)] [[CODE](https://github.com/askinb/FedCMOO)] - Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. [[PUB](https://proceedings.mlr.press/v258/mangold25a.html)] [[CODE](https://pmangold.fr/papers/fed-richardson-romberg/supplementary.zip)] - Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning. [[PUB](https://proceedings.mlr.press/v258/zhang25l.html)] [[CODE](https://github.com/Bernie0115/LR-BPFL)] - On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients. [[PUB](https://proceedings.mlr.press/v258/keshri25a.html)] [[CODE](https://github.com/SatishKeshri/Continual_FL)] - DPFL: Decentralized Personalized Federated Learning. [[PUB](https://proceedings.mlr.press/v258/kharrat25a.html)] [[CODE](https://github.com/salmakh1/DPFL)] - Unbiased Quantization of the L1 Ball for Communication-Efficient Distributed Mean Estimation. [[PUB](https://proceedings.mlr.press/v258/babu25a.html)] #### AI - FedHM: Efficient federated learning for heterogeneous models via low-rank factorization. [[PUB](https://www.sciencedirect.com/science/article/pii/S0004370225000529)] #### AAAI - Learning Together Securely: Prototype-Based Federated Multi-Modal Hashing for Safe and Efficient Multi-Modal Retrieval. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34475)] - Single-Loop Federated Actor-Critic across Heterogeneous Environments. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34469)] - Improving Federated Domain Generalization Through Dynamical Weights Calculated from Data Influences on Global Model Update. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34468)] - FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34464)] - FedGOG: Federated Graph Out-of-Distribution Generalization with Diffusion Data Exploration and Latent Embedding Decorrelation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34459)] - ConFREE: Conflict-free Client Update Aggregation for Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34449)] - Personalized Label Inference Attack in Federated Transfer Learning via Contrastive Meta Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34438)] - Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33455)] - Asynchronous Federated Clustering with Unknown Number of Clusters. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34429)] - Generating Synthetic Data for Unsupervised Federated Learning of Cross-Modal Retrieval. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34415)] - HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34409)] - LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34404)] - Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33440)] - Modeling Inter-Intra Heterogeneity for Graph Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34378)] - pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34368)] - First-Order Federated Bilevel Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34355)] - GAS: Generative Activation-Aided Asynchronous Split Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35503)] - FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35497)] - Federated Graph Condensation with Information Bottleneck Principles. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33417)] - A High-Efficiency Federated Learning Method Using Complementary Pruning for D2D Communication (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35318)] - Federated Learning with Sample-level Client Drift Mitigation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35480)] - Pilot: Building the Federated Multimodal Instruction Tuning Framework. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35476)] - Flexible Sharpness-Aware Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35475)] - MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32076)] - PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar Classifiers. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35460)] - Federated Graph Anomaly Detection Through Contrastive Learning with Global Negative Pairs. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35458)] - Fed-DFA: Federated Distillation for Heterogeneous Model Fusion Through the Adversarial Lens. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35444)] - Federated Recommendation with Explicitly Encoding Item Bias. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33395)] - Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34733)] - Decentralized Federated Learning with Model Caching on Mobile Agents. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35429)] - Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35426)] - FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35423)] - Reinforcement Active Client Selection for Federated Heterogeneous Graph Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35409)] - Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35405)] - Federated Weakly Supervised Video Anomaly Detection with Multimodal Prompt. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35398)] - Overcoming Heterogeneous Data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32807)] - Reputation-aware Revenue Allocation for Auction-based Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34296)] - Learn How to Query from Unlabeled Data Streams in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34287)] - Efficient Federated Learning via Clients-to-Server Knowledge Distillation (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35304)] - Graph Consistency and Diversity Measurement for Federated Multi-View Clustering. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34277)] - WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34272)] - Label-Free Backdoor Attacks in Vertical Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34246)] - Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35054)] - FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34228)] - Fair Federated Survival Analysis. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34214)] - Federated t-SNE and UMAP for Distributed Data Visualization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34204)] - Cross-Silo Feature Space Alignment for Federated Learning on Clients with Imbalanced Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34201)] - Federated Unsupervised Domain Generalization Using Global and Local Alignment of Gradients. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34197)] - In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34192)] - Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34187)] - Breaking Data Silos in Parkinson’s Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33572)] - Federated Unlearning with Gradient Descent and Conflict Mitigation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34181)] - Dual-calibrated Co-training Framework for Personalized Federated Semi-Supervised Medical Image Segmentation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32671)] - FedSPU: Personalized Federated Learning for Resource-Constrained Devices with Stochastic Parameter Update. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34172)] - FedSum: Data-Efficient Federated Learning Under Data Scarcity Scenario for Text Summarization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34129)] - Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34126)] - FedCross: Intertemporal Federated Learning Under Evolutionary Games. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34104)] - Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34094)] - SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34090)] - Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33328)] - Federated Graph-Level Clustering Network. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34077)] - LiD-FL: Towards List-Decodable Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34072)] - Convergence Analysis of Federated Learning Methods Using Backward Error Analysis. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34060)] - Progressive Distribution Matching for Federated Semi-Supervised Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32551)] - TTA-FedDG: Leveraging Test-Time Adaptation to Address Federated Domain Generalization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34053)] - Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34047)] - EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34046)] - FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/34007)] - pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33980)] - FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33975)] - FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33942)] - Federated Learning with Heterogeneous LLMs: Integrating Small Student Client Models with a Large Hungry Model. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35332)] - PA3Fed: Period-Aware Adaptive Aggregation for Improved Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33912)] - TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33524)] - FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33878)] - DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33877)] - Federated Assemblies. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33520)] - Federated Causally Invariant Feature Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33866)] [[CODE](https://github.com/Xianjie-Guo/FedCIFL)] - A New Federated Learning Framework Against Gradient Inversion Attacks. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33865)] - Exploring Vacant Classes in Label-Skewed Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33864)] - Capture Global Feature Statistics for One-Shot Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33862)] - Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33839)] - MFL-Owner: Ownership Protection for Multi-modal Federated Learning via Orthogonal Transform Watermark. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/32313)] - Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33830)] - Beyond Federated Prototype Learning: Learnable Semantic Anchors with Hyperspherical Contrast for Domain-Skewed Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33829)] - Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33822)] - SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33820)] - Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33814)] - How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33788)] - Attribute Inference Attacks for Federated Regression Tasks. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33787)] - Federated Binary Matrix Factorization Using Proximal Optimization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33773)] - Creating Coherence in Federated Non-Negative Matrix Factorization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33772)] - Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33764)] - DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33746)] - Federated Foundation Models on Heterogeneous Time Series. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33739)] - FedPop: Federated Population-based Hyperparameter Tuning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33732)] - Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/35005)] - EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33700)] - Little Is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/33678)] - Breaking Data Silos in Parkinson's Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis. [[PUB](https://doi.org/10.1609/aaai.v39i13.33572)] - FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v39i17.33942)] - Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection. [[PUB](https://doi.org/10.1609/aaai.v39i1.32109)] - Collaborative Learning for 3D Hand-Object Reconstruction and Compositional Action Recognition from Egocentric RGB Videos Using Superquadrics. [[PUB](https://doi.org/10.1609/aaai.v39i7.32800)] - DSRC: Learning Density-Insensitive and Semantic-Aware Collaborative Representation Against Corruptions. [[PUB](https://doi.org/10.1609/aaai.v39i9.33078)] - Learning to Collaborate with Unknown Agents in the Absence of Reward. [[PUB](https://doi.org/10.1609/aaai.v39i13.33589)] - MergeNet: Knowledge Migration Across Heterogeneous Models, Tasks, and Modalities. [[PUB](https://doi.org/10.1609/aaai.v39i5.32510)] - Multi-concept Model Immunization through Differentiable Model Merging. [[PUB](https://doi.org/10.1609/aaai.v39i10.33145)] - Multi-View Collaborative Learning Network for Speech Deepfake Detection. [[PUB](https://doi.org/10.1609/aaai.v39i1.32094)] - Multimodal Promptable Token Merging for Diffusion Models. [[PUB](https://doi.org/10.1609/aaai.v39i16.33894)] - Paid with Models: Optimal Contract Design for Collaborative Machine Learning. [[PUB](https://doi.org/10.1609/aaai.v39i13.33552)] - The Dynamic Duo of Collaborative Masking and Target for Advanced Masked Autoencoder Learning. [[PUB](https://doi.org/10.1609/aaai.v39i18.34145)] - Towards Efficient Collaboration via Graph Modeling in Reinforcement Learning. [[PUB](https://doi.org/10.1609/aaai.v39i16.33813)] ### 2024 #### alt - Optimal Regret Bounds for Collaborative Learning in Bandits. [[PUB](https://proceedings.mlr.press/v237/shidani24a.html)] #### IJCAI - Federated Multi-View Clustering via Tensor Factorization. [[PUB](https://www.ijcai.org/proceedings/2024/438)] - Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means. [[PUB](https://www.ijcai.org/proceedings/2024/439)] - LG-FGAD: An Effective Federated Graph Anomaly Detection Framework. [[PUB](https://www.ijcai.org/proceedings/2024/416)] - Federated Prompt Learning for Weather Foundation Models on Devices. [[PUB](https://www.ijcai.org/proceedings/2024/638)] - Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning via Knowledge Distillation. [[PUB](https://www.ijcai.org/proceedings/2024/419)] - Unlearning during Learning: An Efficient Federated Machine Unlearning Method. [[PUB](https://www.ijcai.org/proceedings/2024/446)] - Practical Hybrid Gradient Compression for Federated Learning Systems. [[PUB](https://www.ijcai.org/proceedings/2024/458)] - Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection. [[PUB](https://www.ijcai.org/proceedings/2024/450)] [[CODE](https://github.com/Xianjie-Guo/FedACD)] - Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains. [[PUB](https://www.ijcai.org/proceedings/2024/457)] [[CODE](https://github.com/LonelyMoonDesert/FNR-FL)] - Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. [[PUB](https://www.ijcai.org/proceedings/2024/788)] - FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients. [[PUB](https://www.ijcai.org/proceedings/2024/501)] - DarkFed: A Data-Free Backdoor Attack in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/491)] - Scalable Federated Unlearning via Isolated and Coded Sharding. [[PUB](https://www.ijcai.org/proceedings/2024/503)] - Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning. [[PUB](https://www.ijcai.org/proceedings/2024/238)] - Label Leakage in Vertical Federated Learning: A Survey. [[PUB](https://www.ijcai.org/proceedings/2024/902)] - The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence. [[PUB](https://www.ijcai.org/proceedings/2024/980)] - LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game. [[PUB](https://www.ijcai.org/proceedings/2024/515)] - EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/51)] - Knowledge Distillation in Federated Learning: A Practical Guide. [[PUB](https://www.ijcai.org/proceedings/2024/905)] - FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization. [[PUB](https://www.ijcai.org/proceedings/2024/526)] - FedPFT: Federated Proxy Fine-Tuning of Foundation Models. [[PUB](https://www.ijcai.org/proceedings/2024/531)] [[CODE](https://github.com/pzp-dzd/FedPFT)] - A Systematic Survey on Federated Semi-supervised Learning. [[PUB](https://www.ijcai.org/proceedings/2024/911)] - Intelligent Agents for Auction-based Federated Learning: A Survey. [[PUB](https://www.ijcai.org/proceedings/2024/912)] - A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/552)] - Dual Calibration-based Personalised Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/551)] - Stakeholder-oriented Decision Support for Auction-based Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/972)] - Redefining Contributions: Shapley-Driven Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/554)] [[CODE](https://github.com/tnurbek/shapfed}{https://github.com/tnurbek/shapfed)] - A Survey on Efficient Federated Learning Methods for Foundation Model Training. [[PUB](https://www.ijcai.org/proceedings/2024/919)] - From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching. [[PUB](https://www.ijcai.org/proceedings/2024/575)] [[CODE](https://github.com/wnn2000/FFL4MIA)] - FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/585)] - FedFa: A Fully Asynchronous Training Paradigm for Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/584)] - FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/594)] - FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise. [[PUB](https://www.ijcai.org/proceedings/2024/599)] - Personalized Federated Learning for Cross-City Traffic Prediction. [[PUB](https://www.ijcai.org/proceedings/2024/611)] - Federated Adaptation for Foundation Model-based Recommendations. [[PUB](https://www.ijcai.org/proceedings/2024/603)] - BADFSS: Backdoor Attacks on Federated Self-Supervised Learning. [[PUB](https://www.ijcai.org/proceedings/2024/61)] - Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning. [[PUB](https://www.ijcai.org/proceedings/2024/290)] [[CODE](https://github.com/GuogangZhu/FedDB)] - FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2024/632)] - Graph Collaborative Expert Finding with Contrastive Learning. [[PUB](https://www.ijcai.org/proceedings/2024/253)] #### AISTATS - BOBA: Byzantine-Robust Federated Learning with Label Skewness. [[PUB](https://proceedings.mlr.press/v238/bao24a.html)] [[PDF](https://arxiv.org/abs/2208.12932)] [[CODE](https://github.com/baowenxuan/BOBA)] - Federated Linear Contextual Bandits with Heterogeneous Clients. [[PUB](https://proceedings.mlr.press/v238/blaser24a.html)] [[PDF](https://arxiv.org/abs/2403.00116)] [[CODE](https://github.com/blaserethan/HetoFedBandit)] - Federated Experiment Design under Distributed Differential Privacy. [[PUB](https://proceedings.mlr.press/v238/chen24c.html)] [[PDF](https://arxiv.org/abs/2311.04375)] [[CODE](https://drive.google.com/file/d/1ugYQQEIOwqc1oH8cUe6rf1mV91c-cF_g/view?usp=drive_link)] - Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression. [[PUB](https://proceedings.mlr.press/v238/chen24d.html)] [[PDF](https://arxiv.org/abs/2310.19059)] - Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. [[PUB](https://proceedings.mlr.press/v238/even24a.html)] [[PDF](https://arxiv.org/abs/2311.00465)] - SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. [[PUB](https://proceedings.mlr.press/v238/fraboni24a.html)] [[PDF](https://arxiv.org/abs/2211.11656)] [[CODE](https://github.com/Accenture/Labs-Federated-Learning/tree/SIFU)] - Compression with Exact Error Distribution for Federated Learning. [[PUB](https://proceedings.mlr.press/v238/hegazy24a.html)] [[PDF](https://arxiv.org/abs/2310.20682)] [[CODE](https://github.com/mahegz/CompWithExactError)] - Adaptive Federated Minimax Optimization with Lower Complexities. [[PUB](https://proceedings.mlr.press/v238/huang24c.html)] [[PDF](https://arxiv.org/abs/2211.07303)] - Adaptive Compression in Federated Learning via Side Information. [[PUB](https://proceedings.mlr.press/v238/isik24a.html)] [[PDF](https://arxiv.org/abs/2306.12625)] [[CODE](https://github.com/FrancescoPase/Federated-KLMS)] - On-Demand Federated Learning for Arbitrary Target Class Distributions. [[PUB](https://proceedings.mlr.press/v238/jeong24a.html)] [[CODE](https://github.com/eai-lab/On-DemandFL)] - FedFisher: Leveraging Fisher Information for One-Shot Federated Learning. [[PUB](https://proceedings.mlr.press/v238/jhunjhunwala24a.html)] [[PDF](https://arxiv.org/abs/2403.12329)] [[CODE](https://github.com/Divyansh03/FedFisher)] - Queuing dynamics of asynchronous Federated Learning. [[PUB](https://proceedings.mlr.press/v238/leconte24a.html)] [[PDF](https://arxiv.org/abs/2405.00017)] - Personalized Federated X-armed Bandit. [[PUB](https://proceedings.mlr.press/v238/li24a.html)] [[PDF](https://arxiv.org/abs/2310.16323)] [[CODE](https://github.com/WilliamLwj/PyXAB)] - Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks. [[PUB](https://proceedings.mlr.press/v238/molaei24a.html)] [[CODE](https://github.com/AnshThakur/FL4HeterogenousEHRs)] - Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization. [[PUB](https://proceedings.mlr.press/v238/shen24c.html)] [[PDF](https://arxiv.org/abs/2311.00944)] - Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters. [[PUB](https://proceedings.mlr.press/v238/sun24a.html)] [[PDF](https://arxiv.org/abs/2306.03824)] [[CODE](https://github.com/fedcodexx/Generalization-of-Federated-Learning)] - Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. [[PUB](https://proceedings.mlr.press/v238/tsoy24a.html)] [[PDF](https://arxiv.org/abs/2403.06672)] [[CODE](https://github.com/nikita-tsoy98/mutually-beneficial-federated-learning-replication)] - Analysis of Privacy Leakage in Federated Large Language Models. [[PUB](https://proceedings.mlr.press/v238/vu24a.html)] [[PDF](https://arxiv.org/abs/2403.04784)] [[CODE](https://github.com/vunhatminh/FL_Attacks.git)] - Invariant Aggregator for Defending against Federated Backdoor Attacks. [[PUB](https://proceedings.mlr.press/v238/wang24e.html)] [[PDF](https://arxiv.org/abs/2210.01834)] [[CODE](https://github.com/Xiaoyang-Wang/InvariantAggregator)] - Communication-Efficient Federated Learning With Data and Client Heterogeneity. [[PUB](https://proceedings.mlr.press/v238/zakerinia24a.html)] [[PDF](https://arxiv.org/abs/2206.10032)] [[CODE](https://github.com/ShayanTalaei/QuAFL)] #### AAAI - FedMut: Generalized Federated Learning via Stochastic Mutation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29146)] - Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29562)] [[PAGE](https://underline.io/lecture/93915-federated-partial-label-learning-with-local-adaptive-augmentation-and-regularization)] - No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28950)] [[PAGE](https://underline.io/lecture/93775-no-prejudice-fair-federated-graph-neural-networks-for-personalized-recommendation)] [[PDF](https://arxiv.org/abs/2312.10080)] [[CODE](https://github.com/nimeshagrawal/F2PGNN-AAAI24)] - Formal Logic Enabled Personalized Federated Learning through Property Inference. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28962)] [[PDF](https://arxiv.org/abs/2401.07448)] - Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28965)] [[PAGE](https://underline.io/lecture/91722-task-agnostic-privacy-preserving-representation-learning-for-federated-learning-against-attribute-inference-attacks)] [[PDF](https://arxiv.org/abs/2312.06989)] [[CODE](https://github.com/TAPPFL/TAPPFL)] - FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28971)] [[PAGE](https://underline.io/lecture/93537-fairtrade-achieving-pareto-optimal-trade-offs-between-balanced-accuracy-and-fairness-in-federated-learning)] - Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28974)] [[PAGE](https://underline.io/lecture/92397-combating-data-imbalances-in-federated-semi-supervised-learning-with-dual-regulators)] [[PDF](https://arxiv.org/abs/2307.05358)] - Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29025)] [[PAGE](https://underline.io/lecture/93417-fed-qssl-a-framework-for-personalized-federated-learning-under-bitwidth-and-data-heterogeneity)] [[PDF](https://arxiv.org/abs/2312.13380)] - On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29010)] - FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29007)] [[PAGE](https://underline.io/lecture/91710-feddat-an-approach-for-foundation-model-finetuning-in-multi-modal-heterogeneous-federated-learning)] [[PDF](https://arxiv.org/abs/2308.12305)] [[CODE](https://github.com/HaokunChen245/FedDAT)] - Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29012)] [[PAGE](https://underline.io/lecture/91776-watch-your-head-assembling-projection-heads-to-save-the-reliability-of-federated-models)] [[PDF](https://arxiv.org/abs/2402.16255)] - FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29031)] [[PAGE](https://underline.io/lecture/92275-fedgcr-achieving-performance-and-fairness-for-federated-learning-with-distinct-client-types-via-group-customization-and-reweighting)] [[CODE](https://github.com/celinezheng/fedgcr)] - Federated Modality-Specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/27909)] [[PAGE](https://underline.io/lecture/91824-federated-modality-specific-encoders-and-multimodal-anchors-for-personalized-brain-tumor-segmentation)] [[PDF](https://arxiv.org/abs/2403.11803)] [[CODE](https://github.com/qdaiing/fedmema)] - Exploiting Label Skews in Federated Learning with Model Concatenation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29063)] [[PAGE](https://underline.io/lecture/92569-exploiting-label-skews-in-federated-learning-with-model-concatenation)] [[PDF](https://arxiv.org/abs/2312.06290)] [[CODE](https://github.com/sjtudyq/FedConcat)] - Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29958)] [[PAGE](https://underline.io/lecture/92937-complementary-knowledge-distillation-for-robust-and-privacy-preserving-model-serving-in-vertical-federated-learning)] - Federated Learning via Input-Output Collaborative Distillation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30209)] [[PAGE](https://underline.io/lecture/94089-federated-learning-via-input-output-collaborative-distillation)] [[PDF](https://arxiv.org/abs/2312.14478)] [[CODE](https://github.com/lsl001006/fediod)] - Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29122)] [[PAGE](https://underline.io/lecture/92727-calibrated-one-round-federated-learning-with-bayesian-inference-in-the-predictive-space)] [[PDF](https://arxiv.org/abs/2312.09817)] [[CODE](https://github.com/hasanmohsin/betaPredBayesFL)] - FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29113)] [[PDF](https://github.com/Xianjie-Guo/FedCSL)] [[CODE](https://github.com/Xianjie-Guo/FedCSL)] - FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29179)] [[PAGE](https://underline.io/lecture/92327-fedfixer-mitigating-heterogeneous-label-noise-in-federated-learning)] [[PDF](https://arxiv.org/abs/2403.16561)] - FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29181)] [[PAGE](https://underline.io/lecture/93122-fedlps-heterogeneous-federated-learning-for-multiple-tasks-with-local-parameter-sharing)] [[PDF](https://arxiv.org/abs/2402.08578)] [[CODE](https://github.com/jyzgh/FedLPS)] - Provably Convergent Federated Trilevel Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29190)] [[PDF](https://arxiv.org/abs/2312.11835)] - Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29191)] [[PAGE](https://underline.io/lecture/93963-performative-federated-learning-a-solution-to-model-dependent-and-heterogeneous-distribution-shifts)] - General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized Services of Multi-Merchants. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30309)] [[PAGE](https://underline.io/lecture/91475-general-commerce-intelligence-glocally-federated-nlp-based-engine-for-privacy-preserving-and-sustainable-personalized-services-of-multi-merchants)] - EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29258)] [[PAGE](https://underline.io/lecture/91709-emgan-early-mix-gan-on-extracting-server-side-model-in-split-federated-learning)] [[CODE](https://github.com/zlijingtao/SFL-MEA)] - FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28095)] [[PAGE](https://underline.io/lecture/91764-feddiv-collaborative-noise-filtering-for-federated-learning-with-noisy-labels)] [[PDF](https://arxiv.org/abs/2312.12263)] [[CODE](https://github.com/lijichang/FLNL-FedDiv)] - Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28082)] [[PAGE](https://underline.io/lecture/92706-point-transformer-with-federated-learning-for-predicting-breast-cancer-her2-status-from-hematoxylin-and-eosin-stained-whole-slide-images)] [[PDF](https://arxiv.org/abs/2312.06454)] [[CODE](https://github.com/Boyden/PointTransformerFL)] - FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29254)] [[PDF](https://arxiv.org/abs/2401.02734)] [[CODE](https://github.com/superlj666/fedns)] - Federated X-armed Bandit. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29267)] [[PAGE](https://underline.io/lecture/93049-federated-x-armed-bandit)] [[PDF](https://arxiv.org/abs/2205.15268)] [[CODE](https://github.com/williamlwj/pyxab)] - Algorithmic Foundation of Federated Learning with Sequential Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30291)] - UFDA: Universal Federated Domain Adaptation with Practical Assumptions. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29311)] [[PAGE](https://underline.io/lecture/93578-ufda-universal-federated-domain-adaptation-with-practical-assumptions)] [[PDF](https://arxiv.org/abs/2311.15570)] [[CODE](https://github.com/Xinhui-99/UFDA)] - FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29297)] [[PAGE](https://underline.io/lecture/92855-fedasmu-efficient-asynchronous-federated-learning-with-dynamic-staleness-aware-model-update)] [[PDF](https://arxiv.org/abs/2312.05770)] - Language-Guided Transformer for Federated Multi-Label Classification. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29295)] [[PAGE](https://underline.io/lecture/93447-language-guided-transformer-for-federated-multi-label-classification)] [[PDF](https://arxiv.org/abs/2312.07165)] [[CODE](https://github.com/Jack24658735/FedLGT)] - FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28175)] [[PAGE](https://underline.io/lecture/92166-fedcd-federated-semi-supervised-learning-with-class-awareness-balance-via-dual-teachers)] [[CODE](https://github.com/YunzZ-Liu/FedCD/)] - Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30131)] [[PAGE](https://underline.io/lecture/94230-beyond-traditional-threats-a-persistent-backdoor-attack-on-federated-learning)] [[CODE](https://github.com/PhD-TaoLiu/FCBA)] - Federated Learning with Extremely Noisy Clients via Negative Distillation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29329)] [[PAGE](https://underline.io/lecture/93309-federated-learning-with-extremely-noisy-clients-via-negative-distillation)] [[PDF](https://arxiv.org/abs/2312.12703)] [[CODE](https://github.com/linChen99/FedNed)] - FedST: Federated Style Transfer Learning for Non-IID Image Segmentation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28199)] [[PAGE](https://underline.io/lecture/93609-fedst-federated-style-transfer-learning-for-non-iid-image-segmentation)] [[学报](https://journal.bupt.edu.cn/CN/abstract/abstract5178.shtml)] [[CODE](https://github.com/YoferChen/FedST)] - PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29339)] [[PAGE](https://underline.io/lecture/92243-ppidsg-a-privacy-preserving-image-distribution-sharing-scheme-with-gan-in-federated-learning)] [[PDF](https://arxiv.org/abs/2312.10380)] [[CODE](https://github.com/ytingma/PPIDSG)] - A Privacy Preserving Federated Learning (PPFL) Based Cognitive Digital Twin (CDT) Framework for Smart Cities. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30400)] - A Primal-Dual Algorithm for Hybrid Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29363)] [[PAGE](https://underline.io/lecture/93144-a-primal-dual-algorithm-for-hybrid-federated-learning)] [[PDF](https://arxiv.org/abs/2210.08106)] - FedLF: Layer-Wise Fair Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29368)] [[PAGE](https://underline.io/lecture/93087-fedlf-layer-wise-fair-federated-learning)] [[CODE](https://github.com/zibinpan/FedLF)] - Towards Fair Graph Federated Learning via Incentive Mechanisms. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29365)] [[PAGE](https://underline.io/lecture/92583-towards-fair-graph-federated-learning-via-incentive-mechanisms)] [[PDF](https://arxiv.org/abs/2312.13306)] [[CODE](https://github.com/Chenglu0426/FairGraphFL)] - Towards the Robustness of Differentially Private Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29967)] [[PAGE](https://underline.io/lecture/92491-towards-the-robustness-of-differentially-private-federated-learning)] - Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29385)] [[PAGE](https://underline.io/lecture/94020-resisting-backdoor-attacks-in-federated-learning-via-bidirectional-elections-and-individual-perspective)] [[PDF](https://arxiv.org/abs/2309.16456)] [[CODE](https://github.com/zhenqincn/Snowball)] - Integer Is Enough: When Vertical Federated Learning Meets Rounding. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29388)] [[PAGE](https://underline.io/lecture/93362-integer-is-enough-when-vertical-federated-learning-meets-rounding)] - CLIP-Guided Federated Learning on Heterogeneity and Long-Tailed Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29416)] [[PAGE](https://underline.io/lecture/92441-clip-guided-federated-learning-on-heterogeneity-and-long-tailed-data)] [[PDF](https://arxiv.org/abs/2312.08648)] [[CODE](https://github.com/shijiangming1/CLIP2FL)] - Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29434)] [[PAGE](https://underline.io/lecture/92772-federated-adaptive-prompt-tuning-for-multi-domain-collaborative-learning)] [[PDF](https://arxiv.org/abs/2211.07864)] [[CODE](https://github.com/leondada/fedapt)] - Multi-Dimensional Fair Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29430)] [[PAGE](https://underline.io/lecture/92619-multi-dimensional-fair-federated-learning)] [[PDF](https://arxiv.org/abs/2312.05551)] - HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30317)] - On the Role of Server Momentum in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29439)] [[PDF](https://arxiv.org/abs/2312.12670)] - FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29446)] [[PAGE](https://underline.io/lecture/93158-fedcompetitors-harmonious-collaboration-in-federated-learning-with-competing-participants)] [[PDF](https://arxiv.org/abs/2312.11391)] - z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29454)] [[PAGE](https://underline.io/lecture/93975-z-signfedavg-a-unified-stochastic-sign-based-compression-for-federated-learning)] [[PDF](https://arxiv.org/abs/2302.02589)] - Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29467)] [[PAGE](https://underline.io/lecture/92405-data-disparity-and-temporal-unavailability-aware-asynchronous-federated-learning-for-predictive-maintenance-on-transportation-fleets)] - Federated Graph Learning under Domain Shift with Generalizable Prototypes. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29468)] [[PAGE](https://underline.io/lecture/92526-federated-graph-learning-under-domain-shift-with-generalizable-prototypes)] - TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29481)] [[PAGE](https://underline.io/lecture/91900-turbosvm-fl-boosting-federated-learning-through-svm-aggregation-for-lazy-clients)] [[PDF](https://arxiv.org/abs/2401.12012)] [[CODE](https://github.com/Kasneci-Lab/TurboSVM-FL)] - Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29510)] [[PDF](https://arxiv.org/abs/2401.10272)] [[CODE](https://github.com/weiyikang/FedGM)] - Concealing Sensitive Samples against Gradient Leakage in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30171)] [[PAGE](https://underline.io/lecture/94147-concealing-sensitive-samples-against-gradient-leakage-in-federated-learning)] [[PDF](https://arxiv.org/abs/2209.05724)] [[CODE](https://github.com/JingWu321/DCS-2)] - FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29525)] [[PAGE](https://underline.io/lecture/92926-feda3i-annotation-quality-aware-aggregation-for-federated-medical-image-segmentation-against-heterogeneous-annotation-noise)] [[PDF](https://arxiv.org/abs/2312.12838)] [[CODE](https://github.com/wnn2000/FedAAAI)] - Federated Causality Learning with Explainable Adaptive Optimization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29566)] [[PAGE](https://underline.io/lecture/93217-federated-causality-learning-with-explainable-adaptive-optimization)] [[PDF](https://arxiv.org/abs/2312.05540)] - Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30045)] [[PAGE](https://underline.io/lecture/93664-federated-contextual-cascading-bandits-with-asynchronous-communication-and-heterogeneous-users)] [[PDF](https://arxiv.org/abs/2402.16312)] - Exploring One-Shot Semi-supervised Federated Learning with Pre-trained Diffusion Models. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29568)] [[PDF](https://arxiv.org/abs/2305.04063)] - Diversity-Authenticity Co-constrained Stylization for Federated Domain Generalization in Person Re-identification. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/28468)] [[PAGE](https://underline.io/lecture/91850-diversity-authenticity-co-constrained-stylization-for-federated-domain-generalization-in-person-re-identification)] - PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29576)] [[PAGE](https://underline.io/lecture/92749-perfedrlnas-one-for-all-personalized-federated-neural-architecture-search)] - Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29603)] [[PAGE](https://underline.io/lecture/92183-efficient-asynchronous-federated-learning-with-prospective-momentum-aggregation-and-fine-grained-correction)] - Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/27796)] - FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29617)] [[PAGE](https://underline.io/lecture/91976-fedtgp-trainable-global-prototypes-with-adaptive-margin-enhanced-contrastive-learning-for-data-and-model-heterogeneity-in-federated-learning)] [[PDF](https://arxiv.org/abs/2401.03230)] [[CODE](https://github.com/TsingZ0/FedTGP)] - LR-XFL: Logical Reasoning-Based Explainable Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30179)] [[PDF](https://arxiv.org/abs/2308.12681)] [[CODE](https://github.com/yanci87/lr-xfl)] - A Huber Loss Minimization Approach to Byzantine Robust Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30181)] [[PAGE](https://underline.io/lecture/94170-a-huber-loss-minimization-approach-to-byzantine-robust-federated-learning)] [[PDF](https://arxiv.org/abs/2308.12581)] - Knowledge-Aware Parameter Coaching for Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29651)] [[PAGE](https://underline.io/lecture/92711-knowledge-aware-parameter-coaching-for-personalized-federated-learning)] - Federated Label-Noise Learning with Local Diversity Product Regularization. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/29659)] [[PAGE](https://underline.io/lecture/92697-federated-label-noise-learning-with-local-diversity-product-regularization)] [[SUPP](https://wanglab.sjtu.edu.cn/userfiles/files/Supp_FedLNL.pdf)] - Adapted Weighted Aggregation in Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30557)] - Knowledge Transfer via Compact Model in Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30498)] [[PAGE](https://underline.io/lecture/91519-knowledge-transfer-via-compact-model-in-federated-learning-student-abstract)] - PICSR: Prototype-Informed Cross-Silo Router for Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/30438)] [[PAGE](https://underline.io/lecture/91585-picsr-prototype-informed-cross-silo-router-for-federated-learning-student-abstract)] - Adapted Weighted Aggregation in Federated Learning. [[PUB](https://doi.org/10.1609/aaai.v38i21.30557)] - Collaborative Consortium of Foundation Models for Open-World Few-Shot Learning. [[PUB](https://doi.org/10.1609/aaai.v38i5.28275)] [[CODE](https://github.com/The-Shuai/CO3)] - Collaborative Learning across Heterogeneous Systems with Pre-Trained Models. [[PUB](https://doi.org/10.1609/aaai.v38i20.30284)] - Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis. [[PUB](https://doi.org/10.1609/aaai.v38i3.27983)] - Communication Efficient Distributed Newton Method over Unreliable Networks. [[PUB](https://doi.org/10.1609/aaai.v38i14.29513)] - DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection. [[PUB](https://doi.org/10.1609/aaai.v38i4.28105)] [[CODE](https://github.com/Serenos/DI-V2X)] - Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective. [[PUB](https://doi.org/10.1609/aaai.v38i11.29140)] - Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing. [[PUB](https://doi.org/10.1609/aaai.v38i4.28089)] - High-Fidelity Gradient Inversion in Distributed Learning. [[PUB](https://doi.org/10.1609/aaai.v38i18.29975)] [[CODE](https://github.com/MiLab-HITSZ/2023YeHFGradInv)] - Learn How to See: Collaborative Embodied Learning for Object Detection and Camera Adjusting. [[PUB](https://doi.org/10.1609/aaai.v38i5.28281)] [[CODE](https://github.com/lydonShen/STF)] ### 2023 #### AI - Privacy-preserving graph convolution network for federated item recommendation. [[PUB](https://www.sciencedirect.com/science/article/abs/pii/S000437022300142X)] - Transfer learning for collaborative recommendation with biased and unbiased data. [[PUB](https://doi.org/10.1016/j.artint.2023.103992)] #### AAAI - Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25531)] - Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25611)] [[PDF](https://arxiv.org/abs/2212.05399)] [[CODE](https://github.com/yflyl613/fedrec)] - Incentive-Boosted Federated Crowdsourcing. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25744)] [[PDF](https://arxiv.org/abs/2211.14439)] - Tackling Data Heterogeneity in Federated Learning with Class Prototypes. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25891)] [[PDF](https://arxiv.org/abs/2212.02758)] [[CODE](https://github.com/yutong-dai/fednh)] - FairFed: Enabling Group Fairness in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25911)] [[PDF](https://arxiv.org/abs/2110.00857)] [[解读](https://zhuanlan.zhihu.com/p/613201113)] - Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25955)] [[CODE](https://github.com/illidanlab/FedRBN)] - Complement Sparsification: Low-Overhead Model Pruning for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/25977)] - Almost Cost-Free Communication in Federated Best Arm Identification. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26010)] [[PDF](https://arxiv.org/abs/2208.09215)] - Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26023)] [[PDF](https://arxiv.org/abs/2110.10302)] - Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26083)] - FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26122)] [[CODE](https://github.com/zibinpan/FedMDFG)] - Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26177)] [[PDF](https://arxiv.org/abs/2106.03328)] [[VIDEO](https://slideslive.com/38960185/securing-secure-aggregation-mitigating-multiround-privacy-leakage-in-federated-learning)] [[CODE](https://openreview.net/attachment?id=nVV6S2sb_UL&name=supplementary_material)] - Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26187)] [[PDF](https://arxiv.org/abs/2211.13009)] [[CODE](https://github.com/yuetan031/fedstar)] - Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26197)] [[PDF](https://arxiv.org/abs/2209.10526)] [[CODE](https://github.com/mmorafah/pacfl)] - FedABC: Targeting Fair Competition in Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26203)] [[PDF](https://arxiv.org/abs/2302.07450)] - Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26212)] [[PDF](https://arxiv.org/abs/2212.01519)] - FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26223)] [[PDF](https://arxiv.org/abs/2211.13975)] [[CODE](https://github.com/wwzzz/fedgs)] - Faster Adaptive Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26235)] [[PDF](https://arxiv.org/abs/2212.00974)] - FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26237)] [[CODE](https://github.com/CodePothunter/fednp)] [[VIDEO](https://www.youtube.com/watch?v=3XM_NNvXCBo)] [[SUPP](https://github.com/CodePothunter/fednp/blob/main/appendix.pdf)] - Bayesian Federated Neural Matching That Completes Full Information. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26245)] [[PDF](https://arxiv.org/abs/2211.08010)] - CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26246)] [[PDF](https://arxiv.org/abs/2105.14216)] [[CODE](https://github.com/xjiajiahao/federated-minimax)] - Federated Generative Model on Multi-Source Heterogeneous Data in IoT. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26252)] - DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26271)] - FedALA: Adaptive Local Aggregation for Personalized Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26330)] [[PDF](https://arxiv.org/abs/2212.01197)] [[CODE](https://github.com/tsingz0/fedala)] - Delving into the Adversarial Robustness of Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26331)] [[PDF](https://arxiv.org/abs/2302.09479)] - On the Vulnerability of Backdoor Defenses for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26393)] [[PDF](https://arxiv.org/abs/2301.08170)] [[CODE](https://github.com/jinghuichen/focused-flip-federated-backdoor-attack)] - Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26400)] [[PDF](https://arxiv.org/abs/2304.05516)] - Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. [[PDF](https://arxiv.org/abs/2211.13009)] [[CODE](https://github.com/yuetan031/fedstar)] - FedGS: Federated Graph-based Sampling with Arbitrary Client Availability. [[PDF](https://arxiv.org/abs/2211.13975)] [[CODE](https://github.com/wwzzz/fedgs)] - Incentive-boosted Federated Crowdsourcing. [[PDF](https://arxiv.org/abs/2211.14439)] - DPAUC: Differentially Private AUC Computation in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26770)] [[CODE](https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC)] - Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26836)] - Industry-Scale Orchestrated Federated Learning for Drug Discovery. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26847)] - A Crowd-AI Collaborative Duo Relational Graph Learning Framework towards Social Impact Aware Photo Classification. [[PUB](https://doi.org/10.1609/aaai.v37i12.26711)] - CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning. [[PUB](https://doi.org/10.1609/aaai.v37i13.26893)] - Heterogeneous-Branch Collaborative Learning for Dialogue Generation. [[PUB](https://doi.org/10.1609/aaai.v37i11.26544)] - A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26984)] - Clustered Federated Learning for Heterogeneous Data (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/27049)] - MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26995)] #### AAAI Special Tracks - DPAUC: Differentially Private AUC Computation in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26770)] [[PDF](https://arxiv.org/abs/2208.12294)] [[CODE](https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC)] #### AAAI Special Programs - Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26836)] [[PDF](https://arxiv.org/abs/2302.11485)] - Industry-Scale Orchestrated Federated Learning for Drug Discovery. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26847)] [[PDF](https://arxiv.org/abs/2210.08871)] [[VIDEO](https://www.youtube.com/watch?v=J_RmZhKzBcA)] - A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26984)] - MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/26995)] - Clustered Federated Learning for Heterogeneous Data (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/27049)] #### IJCAI - FedSampling: A Better Sampling Strategy for Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/462)] [[PDF](https://arxiv.org/abs/2306.14245)] [[CODE](https://github.com/taoqi98/FedSampling)] - HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/440)] [[PDF](https://arxiv.org/abs/2307.14384)] - FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/394)] [[PDF](https://arxiv.org/abs/2208.05174)] [[CODE](https://github.com/cyyever/distributed_learning_simulator)] - Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation. [[PUB](https://www.ijcai.org/proceedings/2023/245)] - Federated Graph Semantic and Structural Learning. [[PUB](https://www.ijcai.org/proceedings/2023/426)] - BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/498)] [[PDF](https://arxiv.org/abs/2305.05221)] - FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment. [[PUB](https://www.ijcai.org/proceedings/2023/444)] [[PDF](https://arxiv.org/abs/2305.06124)] - FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation. [[PUB](https://www.ijcai.org/proceedings/2023/418)] [[PDF](https://arxiv.org/abs/2301.12623)] - Globally Consistent Federated Graph Autoencoder for Non-IID Graphs. [[PUB](https://www.ijcai.org/proceedings/2023/419)] [[CODE](https://github.com/gcfgae/GCFGAE)] - Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/474)] - Dual Personalization on Federated Recommendation. [[PUB](https://www.ijcai.org/proceedings/2023/507)] [[PDF](https://arxiv.org/abs/2301.08143)] [[CODE](https://github.com/zhangcx19/ijcai-23-pfedrec)] - FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity. [[PUB](https://www.ijcai.org/proceedings/2023/492)] [[PDF](https://arxiv.org/abs/2305.05230)] [[CODE](https://github.com/wnn2000/fednoro)] - Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/508)] [[PDF](https://arxiv.org/abs/2304.10783)] [[CODE](https://github.com/zhanghangtao/poisoning-attack-on-fl)] - FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks. [[PUB](https://www.ijcai.org/proceedings/2023/412)] [[PDF](https://arxiv.org/abs/2305.09729)] [[CODE](https://github.com/cynricfu/fedhgn)] - FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer. [[PUB](https://www.ijcai.org/proceedings/2023/443)] [[PDF](https://arxiv.org/abs/2306.15347)] - Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data. [[PUB](https://www.ijcai.org/proceedings/2023/393)] [[PDF](https://arxiv.org/abs/2301.09152)] [[CODE](https://github.com/shengchaochen82/metepfl)] - FedBFPT: An Efficient Federated Learning Framework for Bert Further Pre-training. [[PUB](https://www.ijcai.org/proceedings/2023/483)] [[CODE](https://github.com/Hanzhouu/FedBFPT)] - A Survey of Federated Evaluation in Federated Learning. [[PUB](https://doi.org/10.24963/ijcai.2023/758)] - Learn and Sample Together: Collaborative Generation for Graphic Design Layout. [[PUB](https://doi.org/10.24963/ijcai.2023/649)] - Prompt Learns Prompt: Exploring Knowledge-Aware Generative Prompt Collaboration For Video Captioning. [[PUB](https://doi.org/10.24963/ijcai.2023/180)] - SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract). [[PUB](https://doi.org/10.24963/ijcai.2023/772)] - Q-Learning-Based Model Predictive Variable Impedance Control for Physical Human-Robot Collaboration (Extended Abstract). [[PUB](https://doi.org/10.24963/ijcai.2023/790)] #### IJCAI Survey Track - Bayesian Federated Learning: A Survey. [[PDF](https://arxiv.org/abs/2304.13267)] - A Survey of Federated Evaluation in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2023/758)] [[PDF](https://arxiv.org/abs/2305.08070)] #### IJCAI Journal Track - SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract). [[PUB](https://www.ijcai.org/proceedings/2023/772)] #### AISTATS - The communication cost of security and privacy in federated frequency estimation. [[PUB](https://proceedings.mlr.press/v206/chen23e.html)] [[CODE](https://colab.research.google.com/drive/1A3sp42a4RKswxjCOBAXlfUxBzL5IF431?usp=share_link)] - Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout. [[PUB](https://proceedings.mlr.press/v206/dun23a.html)] [[CODE](https://github.com/dunchen/AsyncDrop__Release)] - Federated Learning under Distributed Concept Drift. [[PUB](https://proceedings.mlr.press/v206/jothimurugesan23a.html)] [[CODE](https://github.com/microsoft/FedDrift)] - Characterizing Internal Evasion Attacks in Federated Learning. [[PUB](https://proceedings.mlr.press/v206/kim23a.html)] [[CODE](https://github.com/tj-kim/pFedDef_v1)] - Federated Asymptotics: a model to compare federated learning algorithms. [[PUB](https://proceedings.mlr.press/v206/cheng23b.html)] [[CODE](https://github.com/garyxcheng/personalized-federated-learning)] - Private Non-Convex Federated Learning Without a Trusted Server. [[PUB](https://proceedings.mlr.press/v206/lowy23a.html)] [[CODE](https://github.com/ghafeleb/Private-NonConvex-Federated-Learning-Without-a-Trusted-Server)] - Federated Learning for Data Streams. [[PUB](https://proceedings.mlr.press/v206/marfoq23a.html)] [[CODE](https://github.com/kholam/FedMuL)] - Nothing but Regrets — Privacy-Preserving Federated Causal Discovery. [[PUB](https://proceedings.mlr.press/v206/mian23a.html)] [[CODE](https://eda.rg.cispa.io/prj/peri/)] - Active Membership Inference Attack under Local Differential Privacy in Federated Learning. [[PUB](https://proceedings.mlr.press/v206/nguyen23e.html)] [[CODE](https://github.com/trucndt/ami)] - Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. [[PUB](https://proceedings.mlr.press/v206/plassier23a.html)] - Byzantine-Robust Federated Learning with Optimal Statistical Rates. [[PUB](https://github.com/wanglun1996/secure-robust-federated-learning)] [[CODE](https://github.com/wanglun1996/secure-robust-federated-learning)] - Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity. [[PUB](https://proceedings.mlr.press/v206/lu23a.html)] - FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. [[PUB](https://proceedings.mlr.press/v206/xu23e.html)] ### 2022 #### ai - Q-Learning-based model predictive variable impedance control for physical human-robot collaboration. [[PUB](https://doi.org/10.1016/j.artint.2022.103771)] #### AISTATS - Towards Understanding Biased Client Selection in Federated Learning. [[PUB](https://proceedings.mlr.press/v151/jee-cho22a.html)] [[CODE](https://proceedings.mlr.press/v151/jee-cho22a/jee-cho22a-supp.zip)] - FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. [[PUB](https://proceedings.mlr.press/v151/gasanov22a.html)] [[PDF](https://arxiv.org/abs/2111.11556)] [[CODE](https://proceedings.mlr.press/v151/gasanov22a/gasanov22a-supp.zip)] - Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective. [[PUB](https://proceedings.mlr.press/v151/glasgow22a.html)] [[PDF](https://arxiv.org/abs/2111.03741)] [[CODE](https://github.com/hongliny/sharp-bounds-for-fedavg-and-continuous-perspective)] - Federated Reinforcement Learning with Environment Heterogeneity. [[PUB](https://proceedings.mlr.press/v151/jin22a.html)] [[PDF](https://arxiv.org/abs/2204.02634)] [[CODE](https://github.com/pengyang7881187/fedrl)] - Federated Myopic Community Detection with One-shot Communication. [[PUB](https://proceedings.mlr.press/v151/ke22a.html)] [[PDF](https://arxiv.org/abs/2106.07255)] - Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. [[PUB](https://proceedings.mlr.press/v151/li22e.html)] [[PDF](https://arxiv.org/abs/2110.01463)] [[CODE](https://github.com/cyrilli/Async-LinUCB)] - Towards Federated Bayesian Network Structure Learning with Continuous Optimization. [[PUB](https://proceedings.mlr.press/v151/ng22a.html)] [[PDF](https://arxiv.org/abs/2110.09356)] [[CODE](https://github.com/ignavierng/notears-admm)] - Federated Learning with Buffered Asynchronous Aggregation. [[PUB](https://proceedings.mlr.press/v151/nguyen22b.html)] [[PDF](https://arxiv.org/abs/2106.06639)] [[VIDEO](https://www.youtube.com/watch?v=Ui-OGUAieNY&ab_channel=FederatedLearningOneWorldSeminar)] - Differentially Private Federated Learning on Heterogeneous Data. [[PUB](https://proceedings.mlr.press/v151/noble22a.html)] [[PDF](https://arxiv.org/abs/2111.09278)] [[CODE](https://github.com/maxencenoble/Differential-Privacy-for-Heterogeneous-Federated-Learning)] - SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. [[PUB](https://proceedings.mlr.press/v151/panda22a.html)] [[PDF](https://arxiv.org/abs/2112.06274)] [[CODE](https://github.com/sparsefed/sparsefed)] [[VIDEO](https://www.youtube.com/watch?v=TXG7ZScheas&ab_channel=GoogleTechTalks)] - Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. [[PUB](https://proceedings.mlr.press/v151/qian22a.html)] [[PDF](https://arxiv.org/abs/2111.01847)] - Federated Functional Gradient Boosting. [[PUB](https://proceedings.mlr.press/v151/shen22a.html)] [[PDF](https://arxiv.org/abs/2103.06972)] [[CODE](https://github.com/shenzebang/Federated-Learning-Pytorch)] - QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. [[PUB](https://proceedings.mlr.press/v151/vono22a.html)] [[PDF](https://arxiv.org/abs/2106.00797)] [[CODE](https://proceedings.mlr.press/v151/vono22a/vono22a-supp.zip)] [[VIDEO](https://www.youtube.com/watch?v=fY8V184It1g&ab_channel=FederatedLearningOneWorldSeminar)] - Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time. [[PUB](https://proceedings.mlr.press/v151/deng22a.html)] #### IJCAI - Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting **`kg.`**. [[PUB](https://www.ijcai.org/proceedings/2022/273)] [[PDF](https://doi.org/10.48550/arXiv.2205.04692)] [[CODE](https://github.com/zjukg/maker)] - Personalized Federated Learning With a Graph. [[PUB](https://www.ijcai.org/proceedings/2022/357)] [[PDF](https://arxiv.org/abs/2203.00829)] [[CODE](https://github.com/dawenzi098/SFL-Structural-Federated-Learning)] - Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification. [[PUB](https://www.ijcai.org/proceedings/2022/272)] [[PDF](https://arxiv.org/abs/2005.11903)] - Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2022/301)] [[PDF](https://arxiv.org/abs/2110.08394)] [[CODE](https://github.com/ljaiverson/pFL-APPLE)] - Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2022/399)] [[PDF](https://arxiv.org/abs/2204.12703)] - Private Semi-Supervised Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2022/279)] - Continual Federated Learning Based on Knowledge Distillation. [[PUB](https://doi.org/10.24963/ijcai.2022/306)] - Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features. [[PUB](https://www.ijcai.org/proceedings/2022/308)] [[PDF](https://arxiv.org/abs/2204.13399)] [[CODE](https://github.com/shangxinyi/CReFF-FL)] - Federated Multi-Task Attention for Cross-Individual Human Activity Recognition. [[PUB](https://www.ijcai.org/proceedings/2022/475)] - Personalized Federated Learning with Contextualized Generalization. [[PUB](https://www.ijcai.org/proceedings/2022/311)] [[PDF](https://arxiv.org/abs/2106.13044)] - Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection. [[PUB](https://www.ijcai.org/proceedings/2022/106)] [[PDF](https://arxiv.org/abs/2204.13256)] - FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2022/324)] [[PDF](https://arxiv.org/abs/2111.08211)] [[CODE](https://github.com/FederatedAI/research/tree/main/publications/FedCG)] - FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server. [[PUB](https://www.ijcai.org/proceedings/2022/385)] [[PDF](https://arxiv.org/abs/2204.11536)] - Towards Verifiable Federated Learning **`surv.`**. [[PUB](https://www.ijcai.org/proceedings/2022/792)] [[PDF](https://arxiv.org/abs/2202.08310)] - Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting. [[PUB](https://doi.org/10.24963/ijcai.2022/273)] - Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios. [[PUB](https://doi.org/10.24963/ijcai.2022/306)] - Towards Verifiable Federated Learning. [[PUB](https://doi.org/10.24963/ijcai.2022/792)] - A Survey on Gradient Inversion: Attacks, Defenses and Future Directions. [[PUB](https://doi.org/10.24963/ijcai.2022/791)] #### AAAI - HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/19993)] [[PDF](https://arxiv.org/abs/2112.10775)] [[CODE](https://github.com/med-air/HarmoFL)] [[解读](https://zhuanlan.zhihu.com/p/472555067)] - Federated Learning for Face Recognition with Gradient Correction. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20095)] [[PDF](https://arxiv.org/abs/2112.07246)] - SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20643)] [[PDF](https://arxiv.org/abs/2106.02743)] [[CODE](https://github.com/FedML-AI/SpreadGNN)] [[解读](https://zhuanlan.zhihu.com/p/429720860)] - SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20345)] [[CODE](https://github.com/wudonglei99/smartidx)] - Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/19878)] [[PDF](https://arxiv.org/abs/2112.08831)] - Seizing Critical Learning Periods in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20859)] [[PDF](https://arxiv.org/abs/2109.05613)] - Coordinating Momenta for Cross-silo Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20853)] [[PDF](https://arxiv.org/abs/2102.03970)] - FedProto: Federated Prototype Learning over Heterogeneous Devices. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20819)] [[PDF](https://arxiv.org/abs/2105.00243)] [[CODE](https://github.com/yuetan031/fedproto)] - FedSoft: Soft Clustered Federated Learning with Proximal Local Updating. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20785)] [[PDF](https://arxiv.org/abs/2112.06053)] [[CODE](https://github.com/ycruan/FedSoft)] - Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20555)] [[PDF](https://arxiv.org/abs/2112.09824)] [[CODE](https://github.com/bibikar/feddst)] - FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20057)] [[PDF](https://arxiv.org/abs/2112.12496)] [[CODE](https://github.com/jackie840129/fedfr)] - SplitFed: When Federated Learning Meets Split Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20825)] [[PDF](https://arxiv.org/abs/2004.12088)] [[CODE](https://github.com/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning)] - Efficient Device Scheduling with Multi-Job Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21235)] [[PDF](https://arxiv.org/abs/2112.05928)] - Implicit Gradient Alignment in Distributed and Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20597)] [[PDF](https://arxiv.org/abs/2106.13897)] - Federated Nearest Neighbor Classification with a Colony of Fruit-Flies. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20775)] [[PDF](https://arxiv.org/abs/2112.07157)] [[CODE](https://github.com/rithram/flynn)] - A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20894)] - Contribution-Aware Federated Learning for Smart Healthcare. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21505)] - Cross-Modal Federated Human Activity Recognition via Modality-Agnostic and Modality-Specific Representation Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20213)] - CrowdFL: A Marketplace for Crowdsourced Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21715)] - FedInv: Byzantine-Robust Federated Learning by Inversing Local Model Updates. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20903)] - FedProto: Federated Prototype Learning across Heterogeneous Clients. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20819)] - Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/20755)] - Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21446)] - CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation. [[PUB](https://doi.org/10.1609/aaai.v36i2.20107)] - Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving. [[PUB](https://doi.org/10.1609/aaai.v36i3.20149)] - Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD. [[PUB](https://doi.org/10.1609/aaai.v36i8.20832)] - Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. [[PUB](https://doi.org/10.1609/aaai.v36i9.21177)] - Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration. [[PUB](https://doi.org/10.1609/aaai.v36i4.20372)] - AsyncFL: Asynchronous Federated Learning Using Majority Voting with Quantized Model Updates (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21624)] - Class-Wise Adaptive Self Distillation for Federated Learning on Non-IID Data (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21620)] - FedCC: Federated Learning with Consensus Confirmation for Byzantine Attack Resistance (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/21627)] #### ALT - Iterated Vector Fields and Conservatism, with Applications to Federated Learning. [[PUB](https://proceedings.mlr.press/v167/charles22a.html)] [[PDF](https://arxiv.org/abs/2109.03973)] ### 2021 #### IJCAI - Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization. [[PUB](https://www.ijcai.org/proceedings/2021/202)] [[PDF](https://arxiv.org/abs/2008.01558)] [[VIDEO](https://papertalk.org/papertalks/35198)] - Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2021/352)] [[PDF](https://arxiv.org/abs/2106.12300)] - FedSpeech: Federated Text-to-Speech with Continual Learning. [[PUB](https://www.ijcai.org/proceedings/2021/527)] [[PDF](https://arxiv.org/abs/2110.07216)] - Practical One-Shot Federated Learning for Cross-Silo Setting. [[PUB](https://www.ijcai.org/proceedings/2021/205)] [[PDF](https://arxiv.org/abs/2010.01017)] [[CODE](https://github.com/QinbinLi/FedKT)] - Federated Model Distillation with Noise-Free Differential Privacy. [[PUB](https://www.ijcai.org/proceedings/2021/216)] [[PDF](https://arxiv.org/abs/2202.08310)] [[VIDEO](https://papertalk.org/papertalks/35184)] - LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy. [[PUB](https://www.ijcai.org/proceedings/2021/217)] [[PDF](https://arxiv.org/abs/2007.15789)] - Federated Learning with Fair Averaging. :fire:. [[PUB](https://www.ijcai.org/proceedings/2021/223)] [[PDF](https://arxiv.org/abs/2104.14937)] [[CODE](https://github.com/WwZzz/easyFL)] - H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2021/67)] [[PDF](https://arxiv.org/abs/2106.00275)] - Communication-efficient and Scalable Decentralized Federated Edge Learning. [[PUB](https://www.ijcai.org/proceedings/2021/720)] - Federated Learning with Fair Averaging. [[PUB](https://doi.org/10.24963/ijcai.2021/223)] [[CODE](https://github.com/WwZzz/easyFL)] - Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare. [[PUB](https://doi.org/10.24963/ijcai.2021/486)] - Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification. [[PUB](https://doi.org/10.24963/ijcai.2021/135)] [[CODE](https://github.com/Kali-Hac/MG-SCR)] #### AAAI - Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17301)] [[PDF](https://arxiv.org/abs/2103.00958)] [[VIDEO](https://slideslive.com/38947765/secure-bilevel-asynchronous-vertical-federated-learning-with-backward-updating)] - FedRec++: Lossless Federated Recommendation with Explicit Feedback. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16546)] [[VIDEO](https://slideslive.com/38947798/fedrec-lossless-federated-recommendation-with-explicit-feedback)] - Federated Multi-Armed Bandits. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17156)] [[PDF](https://arxiv.org/abs/2101.12204)] [[CODE](https://github.com/ShenGroup/FMAB)] [[VIDEO](https://slideslive.com/38947985/federated-multiarmed-bandits)] - On the Convergence of Communication-Efficient Local SGD for Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16920)] [[VIDEO](https://slideslive.com/38948341/on-the-convergence-of-communicationefficient-local-sgd-for-federated-learning)] - FLAME: Differentially Private Federated Learning in the Shuffle Model. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17053)] [[PDF](https://arxiv.org/abs/2009.08063)] [[VIDEO](https://slideslive.com/38948496/flame-differentially-private-federated-learning-in-the-shuffle-model)] [[CODE](https://github.com/Rachelxuan11/FLAME)] - Toward Understanding the Influence of Individual Clients in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17263)] [[PDF](https://arxiv.org/abs/2012.10936)] [[VIDEO](https://slideslive.com/38948549/toward-understanding-the-influence-of-individual-clients-in-federated-learning)] - Provably Secure Federated Learning against Malicious Clients. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16849)] [[PDF](https://arxiv.org/abs/2102.01854)] [[VIDEO](https://www.youtube.com/watch?v=LP4uqW18yA0&ab_channel=PurdueCERIAS)] [[SLIDE](https://people.duke.edu/~zg70/code/Secure_Federated_Learning.pdf)] - Personalized Cross-Silo Federated Learning on Non-IID Data. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16960)] [[PDF](https://arxiv.org/abs/2007.03797)] [[VIDEO](https://slideslive.com/38948676/personalized-crosssilo-federated-learning-on-noniid-data)] [[UC.](https://github.com/TsingZ0/PFL-Non-IID)] - Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/16669)] [[PDF](https://arxiv.org/abs/2010.00753)] [[CODE](https://github.com/kpdonahue/model_sharing_games)] [[VIDEO](https://slideslive.com/38948684/modelsharing-games-analyzing-federated-learning-under-voluntary-participation)] - Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17291)] [[PDF](https://arxiv.org/abs/2102.00655)] [[VIDEO](https://slideslive.com/38949098/curse-or-redemption-how-data-heterogeneity-affects-the-robustness-of-federated-learning)] - Game of Gradients: Mitigating Irrelevant Clients in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17093)] [[PDF](https://arxiv.org/abs/2110.12257)] [[CODE](https://github.com/nlokeshiisc/sfedavg-aaai21)] [[VIDEO](https://slideslive.com/38949109/game-of-gradients-mitigating-irrelevant-clients-in-federated-learning)] [[SUPP](https://github.com/nlokeshiisc/SFedAvg-AAAI21)] - Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17240)] [[PDF](https://arxiv.org/abs/2012.13900)] [[VIDEO](https://slideslive.com/38949195/federated-block-coordinate-descent-scheme-for-learning-global-and-personalized-models)] [[CODE](https://github.com/REIYANG/FedBCD)] - Addressing Class Imbalance in Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17219)] [[PDF](https://arxiv.org/abs/2008.06217)] [[VIDEO](https://slideslive.com/38949283/adressing-class-imbalance-in-federated-learning)] [[CODE](https://github.com/balanced-fl/Addressing-Class-Imbalance-FL)] [[解读](https://zhuanlan.zhihu.com/p/443009189)] - Defending against Backdoors in Federated Learning with Robust Learning Rate. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17118)] [[PDF](https://arxiv.org/abs/2007.03767)] [[VIDEO](https://slideslive.com/38949344/defending-against-backdoors-in-federated-learning-with-robust-learning-rate)] [[CODE](https://github.com/TinfoilHat0/Defending-Against-Backdoors-with-Robust-Learning-Rate)] - AI-Infused Collaborative Inquiry in Upper Elementary School: A Game-Based Learning Approach. [[PUB](https://doi.org/10.1609/aaai.v35i17.17836)] - Collaborative Group Learning. [[PUB](https://doi.org/10.1609/aaai.v35i8.16911)] - Communication-Aware Collaborative Learning. [[PUB](https://doi.org/10.1609/aaai.v35i8.16838)] - Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning. [[PUB](https://doi.org/10.1609/aaai.v35i12.17246)] - DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning. [[PUB](https://doi.org/10.1609/aaai.v35i2.16204)] - Differentially Private and Communication Efficient Collaborative Learning. [[PUB](https://doi.org/10.1609/aaai.v35i8.16887)] - Peer Collaborative Learning for Online Knowledge Distillation. [[PUB](https://doi.org/10.1609/aaai.v35i12.17234)] - STL-SGD: Speeding Up Local SGD with Stagewise Communication Period. [[PUB](https://doi.org/10.1609/aaai.v35i11.17153)] - A Serverless Approach to Federated Learning Infrastructure Oriented for IoT/Edge Data Sources (Student Abstract). [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/17870)] #### AISTATS - Free-rider Attacks on Model Aggregation in Federated Learning. [[PUB](http://proceedings.mlr.press/v130/fraboni21a.html)] [[PDF](https://arxiv.org/abs/2006.11901)] [[CODE](https://github.com/Accenture/Labs-Federated-Learning)] [[VIDEO](https://papertalk.org/papertalks/27640)] [[SUPP](http://proceedings.mlr.press/v130/fraboni21a/fraboni21a-supp.pdf)] - Federated f-differential privacy. [[PUB](http://proceedings.mlr.press/v130/zheng21a.html)] [[CODE](https://github.com/enosair/federated-fdp)] [[VIDEO](https://papertalk.org/papertalks/27595)] [[SUPP](http://proceedings.mlr.press/v130/zheng21a/zheng21a-supp.pdf)] - Federated learning with compression: Unified analysis and sharp guarantees :fire:. [[PUB](http://proceedings.mlr.press/v130/haddadpour21a.html)] [[PDF](https://arxiv.org/abs/2007.01154)] [[CODE](https://github.com/MLOPTPSU/FedTorch)] [[VIDEO](https://papertalk.org/papertalks/27584)] [[SUPP](http://proceedings.mlr.press/v130/haddadpour21a/haddadpour21a-supp.pdf)] - Shuffled Model of Differential Privacy in Federated Learning. [[PUB](http://proceedings.mlr.press/v130/girgis21a.html)] [[VIDEO](https://papertalk.org/papertalks/27565)] [[SUPP](http://proceedings.mlr.press/v130/girgis21a/girgis21a-supp.pdf)] - Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. [[PUB](http://proceedings.mlr.press/v130/charles21a.html)] [[PDF](https://arxiv.org/abs/2103.05032)] [[VIDEO](https://papertalk.org/papertalks/27559)] [[SUPP](http://proceedings.mlr.press/v130/charles21a/charles21a-supp.pdf)] - Federated Multi-armed Bandits with Personalization. [[PUB](http://proceedings.mlr.press/v130/shi21c.html)] [[PDF](https://arxiv.org/abs/2102.13101)] [[CODE](https://github.com/ShenGroup/PF_MAB)] [[VIDEO](https://papertalk.org/papertalks/27521)] [[SUPP](http://proceedings.mlr.press/v130/shi21c/shi21c-supp.pdf)] - Towards Flexible Device Participation in Federated Learning. [[PUB](http://proceedings.mlr.press/v130/ruan21a.html)] [[PDF](https://arxiv.org/abs/2006.06954)] [[VIDEO](https://papertalk.org/papertalks/27467)] [[SUPP](http://proceedings.mlr.press/v130/ruan21a/ruan21a-supp.pdf)] - Federated Learning with Compression: Unified Analysis and Sharp Guarantees. [[PUB](http://proceedings.mlr.press/v130/haddadpour21a.html)] - Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization. [[PUB](http://proceedings.mlr.press/v130/chen21c.html)] - LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. [[PUB](http://proceedings.mlr.press/v130/shokri-ghadikolaei21a.html)] - Local SGD: Unified Theory and New Efficient Methods. [[PUB](http://proceedings.mlr.press/v130/gorbunov21a.html)] - One-Round Communication Efficient Distributed M-Estimation. [[PUB](http://proceedings.mlr.press/v130/bao21a.html)] ### 2020 #### IJCAI - Federated Meta-Learning for Fraudulent Credit Card Detection. [[PUB](https://www.ijcai.org/proceedings/2020/642)] [[VIDEO](https://www.ijcai.org/proceedings/2020/video/23994)] - A Multi-player Game for Studying Federated Learning Incentive Schemes. [[PUB](https://www.ijcai.org/proceedings/2020/769)] [[CODE](https://github.com/benggggggggg/fedgame)] [[解读](https://zhuanlan.zhihu.com/p/353868739)] - A De Novo Divide-and-Merge Paradigm for Acoustic Model Optimization in Automatic Speech Recognition. [[PUB](https://doi.org/10.24963/ijcai.2020/513)] - CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning. [[PUB](https://doi.org/10.24963/ijcai.2020/467)] - Collaborative Learning of Depth Estimation, Visual Odometry and Camera Relocalization from Monocular Videos. [[PUB](https://doi.org/10.24963/ijcai.2020/68)] #### AAAI - Practical Federated Gradient Boosting Decision Trees. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/5895)] [[PDF](https://arxiv.org/abs/1911.04206)] [[CODE](https://github.com/Xtra-Computing/PrivML)] - Federated Learning for Vision-and-Language Grounding Problems. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/6824)] - Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/6096)] - Federated Patient Hashing. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/6121)] - Robust Federated Learning via Collaborative Machine Teaching. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/5826)] [[PDF](https://arxiv.org/abs/1905.02941)] - FedVision: An Online Visual Object Detection Platform Powered by Federated Learning. [[PUB](https://ojs.aaai.org/index.php/AAAI/article/view/7021)] [[PDF](https://arxiv.org/abs/2001.06202)] [[CODE](https://github.com/FederatedAI/FedVision)] - Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis. [[PUB](https://doi.org/10.1609/aaai.v34i07.6619)] - Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning. [[PUB](https://doi.org/10.1609/aaai.v34i04.5843)] - Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning. [[PUB](https://doi.org/10.1609/aaai.v34i04.5706)] #### AISTATS - FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. [[PUB](http://proceedings.mlr.press/v108/reisizadeh20a.html)] [[PDF](https://arxiv.org/abs/1909.13014)] [[VIDEO](https://papertalk.org/papertalks/7961)] [[SUPP](http://proceedings.mlr.press/v108/reisizadeh20a/reisizadeh20a-supp.pdf)] - How To Backdoor Federated Learning :fire:. [[PUB](http://proceedings.mlr.press/v108/bagdasaryan20a.html)] [[PDF](https://arxiv.org/abs/1807.00459)] [[VIDEO](https://papertalk.org/papertalks/8046)] [[CODE](https://github.com/ebagdasa/backdoor_federated_learning)] [[SUPP](http://proceedings.mlr.press/v108/bagdasaryan20a/bagdasaryan20a-supp.pdf)] - Federated Heavy Hitters Discovery with Differential Privacy. [[PUB](http://proceedings.mlr.press/v108/zhu20a.html)] [[PDF](https://arxiv.org/abs/1902.08534)] [[VIDEO](https://papertalk.org/papertalks/8129)] [[SUPP](http://proceedings.mlr.press/v108/zhu20a/zhu20a-supp.pdf)] - How To Backdoor Federated Learning. [[PUB](http://proceedings.mlr.press/v108/bagdasaryan20a.html)] - Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction. [[PUB](http://proceedings.mlr.press/v108/li20f.html)] - Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. [[PUB](http://proceedings.mlr.press/v108/zantedeschi20a.html)] - Tighter Theory for Local SGD on Identical and Heterogeneous Data. [[PUB](http://proceedings.mlr.press/v108/bayoumi20a.html)] ### 2019 #### aaai - Collaboration Based Multi-Label Learning. [[PUB](https://doi.org/10.1609/aaai.v33i01.33013550)] - Improving Domain-Specific Classification by Collaborative Learning with Adaptation Networks. [[PUB](https://doi.org/10.1609/aaai.v33i01.33015450)] - Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization. [[PUB](https://doi.org/10.1609/aaai.v33i01.33015508)] #### aistats - Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks. [[PUB](http://proceedings.mlr.press/v89/yu19a.html)] - Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication. [[PUB](http://proceedings.mlr.press/v89/acharya19a.html)] #### IJCAI - Multi-Agent Visualization for Explaining Federated Learning. [[PUB](https://www.ijcai.org/proceedings/2019/960)] [[VIDEO](https://youtu.be/NPGf_OJrzOg)] - A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification. [[PUB](https://doi.org/10.24963/ijcai.2019/473)] - Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems. [[PUB](https://doi.org/10.24963/ijcai.2019/619)] - Efficient Protocol for Collaborative Dictionary Learning in Decentralized Networks. [[PUB](https://doi.org/10.24963/ijcai.2019/359)] - Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust. [[PUB](https://doi.org/10.24963/ijcai.2019/538)] - Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees. [[PUB](https://doi.org/10.24963/ijcai.2019/73)] ### 2018 #### aaai - Robust Collaborative Discriminative Learning for RGB-Infrared Tracking. [[PUB](https://doi.org/10.1609/aaai.v32i1.12307)] - Uplink Communication Efficient Differentially Private Sparse Optimization With Feature-Wise Distributed Data. [[PUB](https://doi.org/10.1609/aaai.v32i1.11311)] #### ijcai - Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection. [[PUB](https://doi.org/10.24963/ijcai.2018/285)] - Collaborative and Attentive Learning for Personalized Image Aesthetic Assessment. [[PUB](https://doi.org/10.24963/ijcai.2018/133)] - Collaborative Learning for Weakly Supervised Object Detection. [[PUB](https://doi.org/10.24963/ijcai.2018/135)] - CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering. [[PUB](https://doi.org/10.24963/ijcai.2018/509)] - Keeping in Touch with Collaborative UAVs: A Deep Reinforcement Learning Approach. [[PUB](https://doi.org/10.24963/ijcai.2018/78)] - Learning and Communicating the Latent States of Human-Machine Collaboration. [[PUB](https://doi.org/10.24963/ijcai.2018/838)] ### 2017 #### aistats - Communication-efficient Distributed Sparse Linear Discriminant Analysis. [[PUB](http://proceedings.mlr.press/v54/tian17a.html)] - Decentralized Collaborative Learning of Personalized Models over Networks. [[PUB](http://proceedings.mlr.press/v54/vanhaesebrouck17a.html)] ### 2016 #### ai - H-index manipulation by merging articles: Models, theory, and experiments. [[PUB](https://doi.org/10.1016/j.artint.2016.08.001)] #### aistats - Communication Efficient Distributed Agnostic Boosting. [[PUB](http://proceedings.mlr.press/v51/chen16e.html)] #### ijcai - Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction. [[PUB](http://www.ijcai.org/Abstract/16/424)] - Modeling Contagious Merger and Acquisition via Point Processes with a Profile Regression Prior. [[PUB](http://www.ijcai.org/Abstract/16/382)] ### 2015 #### ijcai - H-Index Manipulation by Merging Articles: Models, Theory, and Experiments. [[PUB](http://ijcai.org/Abstract/15/119)] ### 2014 #### aistats - Scalable Collaborative Bayesian Preference Learning. [[PUB](http://proceedings.mlr.press/v33/khan14.html)] [[CODE](https://github.com/UKPLab/tacl2018-preference-convincing/tree/crowdGPPL)] ### 2013 #### aaai - Learning Collaborative Impedance-Based Robot Behaviors. [[PUB](https://doi.org/10.1609/aaai.v27i1.8543)] #### ai - Transfer learning in heterogeneous collaborative filtering domains. [[PUB](https://doi.org/10.1016/j.artint.2013.01.003)] ### 2012 #### aaai - Transfer Learning in Collaborative Filtering with Uncertain Ratings. [[PUB](https://doi.org/10.1609/aaai.v26i1.8197)] #### ai - Towards mobile intelligence: Learning from GPS history data for collaborative recommendation. [[PUB](https://doi.org/10.1016/j.artint.2012.02.002)] ### 2011 #### aaai - Mechanism Design for Federated Sponsored Search Auctions. [[PUB](http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3576)] ### 2010 #### aaai - Transfer Learning in Collaborative Filtering for Sparsity Reduction. [[PUB](https://doi.org/10.1609/aaai.v24i1.7578)] ### 2007 #### aaai - A Model-based Approach for Merging Prioritized Knowledge Bases in Possibilistic Logic. [[PUB](http://www.aaai.org/Library/AAAI/2007/aaai07-074.php)] - PLOW: A Collaborative Task Learning Agent. [[PUB](http://www.aaai.org/Library/AAAI/2007/aaai07-240.php)] ### 2000 #### alt - Extracting Information from the Web for Concept Learning and Collaborative Filtering. [[PUB](https://doi.org/10.1007/3-540-40992-0_1)]

## fl in top ml conference and journal Federated Learning papers accepted by top ML(machine learning) conference and journal, Including [NeurIPS](https://dblp.uni-trier.de/db/conf/nips/index.html)(Annual Conference on Neural Information Processing Systems), [ICML](https://dblp.uni-trier.de/db/conf/icml/index.html)(International Conference on Machine Learning), [ICLR](https://dblp.uni-trier.de/db/conf/iclr/index.html)(International Conference on Learning Representations), [COLT](https://dblp.org/db/conf/colt/index.html)(Annual Conference Computational Learning Theory) , [UAI](https://dblp.org/db/conf/uai/index.html)(Conference on Uncertainty in Artificial Intelligence),[Machine Learning](https://dblp.org/db/journals/ml/index.html), [JMLR](https://dblp.uni-trier.de/db/journals/jmlr/index.html)(Journal of Machine Learning Research), [TPAMI](https://dblp.uni-trier.de/db/journals/pami/index.html)(IEEE Transactions on Pattern Analysis and Machine Intelligence). - [NeurIPS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANeurIPS%3A) [2024](https://papers.nips.cc/paper_files/paper/2024)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2024/Conference#tab-accept-oral)), [2023](https://papers.nips.cc/paper_files/paper/2023)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2023/Conference#tab-accept-oral)), [2022](https://papers.nips.cc/paper_files/paper/2022)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2022/Conference)), [2021](https://papers.nips.cc/paper/2021)([OpenReview](https://openreview.net/group?id=NeurIPS.cc/2021/Conference)), [2020](https://papers.nips.cc/paper/2020), [2018](https://papers.nips.cc/paper/2018), [2017](https://papers.nips.cc/paper/2017) - [ICML](https://dblp.uni-trier.de/search?q=federate%20venue%3AICML%3A) [2025](https://icml.cc/Conferences/2025/Schedule?type=Poster), [2024](https://icml.cc/Conferences/2024/Schedule?type=Poster), [2023](https://icml.cc/Conferences/2023/Schedule?type=Poster), [2022](https://icml.cc/Conferences/2022/Schedule?type=Poster), [2021](https://icml.cc/Conferences/2021/Schedule?type=Poster), [2020](https://icml.cc/Conferences/2020/Schedule?type=Poster), [2019](https://icml.cc/Conferences/2019/Schedule?type=Poster) - [ICLR](https://dblp.uni-trier.de/search?q=federate%20venue%3AICLR%3A) [2025](https://openreview.net/group?id=ICLR.cc/2025/Conference), [2024](https://openreview.net/group?id=ICLR.cc/2024/Conference), [2023](https://openreview.net/group?id=ICLR.cc/2023/Conference), [2022](https://openreview.net/group?id=ICLR.cc/2022/Conference), [2021](https://openreview.net/group?id=ICLR.cc/2021/Conference), [2020](https://openreview.net/group?id=ICLR.cc/2020/Conference) - [COLT](https://dblp.org/search?q=federated%20venue%3ACOLT%3A) [2023](https://proceedings.mlr.press/v195/) - [UAI](https://dblp.org/search?q=federated%20venue%3AUAI%3A) [2025](https://www.auai.org/uai2025/accepted_papers), [2024](https://www.auai.org/uai2024/accepted_papers), [2023](https://www.auai.org/uai2023/accepted_papers), [2022](https://www.auai.org/uai2022/accepted_papers), [2021](https://www.auai.org/uai2021/accepted_papers) - [Machine Learning](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fml%3A) 2026, 2025, 2024, 2023, 2022 - [JMLR](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fjmlr%3A) 2025([v26](https://jmlr.org/papers/v26/)), 2024([v25](https://jmlr.org/papers/v25/)), 2023([v24](https://jmlr.org/papers/v24/)), 2021([v22](https://jmlr.org/papers/v22/)) - [TPAMI](https://dblp.uni-trier.de/search?q=federated%20streamid%3Ajournals%2Fpami%3A) 2026, 2025, 2024, 2023, 2022

fl in top ml conference and journal

### 2026 #### jmlr - Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information. [[PUB](https://jmlr.org/papers/v27/23-0440.html)] #### Machine Learning - FDGReID: Federated Domain Generalization for Person Re-identification. [[PUB](https://doi.org/10.1007/s10994-025-06974-z)] - FedBNR: A Fully Global Federated Gaussian Process. [[PUB](https://doi.org/10.1007/s10994-025-06936-5)] - Federated Learning on Riemannian Manifolds with Differential Privacy. [[PUB](https://doi.org/10.1007/s10994-026-07018-w)] - Federated SHAP: Privacy-Preserving and Consistent Post-hoc Explainability in Federated Learning. [[PUB](https://doi.org/10.1007/s10994-025-06956-1)] - FedGES: A Federated Learning Approach for Bayesian Network Structure Learning. [[PUB](https://doi.org/10.1007/s10994-025-06939-2)] - Collaborative Multivariate Time Series Forecasting via Variable-Tailored Inter-temporal Graph and Adaptive-Smooth Frequency Fusion. [[PUB](https://doi.org/10.1007/s10994-025-06963-2)] - PC-MoE: memory-efficient and privacy-preserving collaborative training for Mixture-of-Experts LLMs. [[PUB](https://doi.org/10.1007/s10994-025-06901-2)] #### TPAMI - A Bayesian Framework for Clustered Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3637562)] - Adaptive Batch Size Time Evolving Stochastic Gradient Descent for Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3610169)] - Communication-Efficient Federated Multi-View Clustering. [[PUB](https://doi.org/10.1109/TPAMI.2025.3601533)] - Decentralized Federated Learning With Distributed Aggregation Weight Optimization. [[PUB](https://doi.org/10.1109/TPAMI.2025.3640709)] - Exploring the Vulnerabilities of Federated Learning: A Deep Dive Into Gradient Inversion Attacks. [[PUB](https://doi.org/10.1109/TPAMI.2025.3646639)] - FedFask: Fast Sketching Distributed PCA for Large-Scale Federated Data. [[PUB](https://doi.org/10.1109/TPAMI.2025.3639635)] - Sample-Level Prototypical Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3612302)] - Slack Federated Adversarial Training. [[PUB](https://doi.org/10.1109/TPAMI.2025.3646649)] - Toward Understanding Generalization and Stability Gaps Between Centralized and Decentralized Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3647762)] - Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging. [[PUB](https://doi.org/10.1109/TPAMI.2025.3629605)] ### 2025 #### JMLR - Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback. [[PUB](https://jmlr.org/papers/v26/24-0385.html)] - Client Selection for Federated Policy Optimization with Environment Heterogeneity. [[PUB](https://jmlr.org/papers/v26/24-0233.html)] - FedHB: Hierarchical Bayesian Federated Learning. [[PUB](https://jmlr.org/papers/v26/23-1350.html)] - PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark. [[PUB](https://jmlr.org/papers/v26/23-1634.html)] - Sharp Bounds for Sequential Federated Learning on Heterogeneous Data. [[PUB](https://jmlr.org/papers/v26/24-0668.html)] - Collaborative likelihood-ratio estimation over graphs. [[PUB](https://jmlr.org/papers/v26/24-0565.html)] #### machine learning - Auction-based incentive mechanism with personalized privacy protection in federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06836-8)] - DP-FedSecure: a secure and efficient federated learning scheme based on adaptive differential privacy. [[PUB](https://doi.org/10.1007/s10994-025-06888-w)] - Efficient federated unlearning under plausible deniability. [[PUB](https://doi.org/10.1007/s10994-024-06685-x)] [[CODE](https://github.com/Ayush-Umu/Federated-Unlearning-under-Plausible-Deniability)] - Federated causal inference from observational data. [[PUB](https://doi.org/10.1007/s10994-025-06819-9)] - Fedflow: a personalized federated learning framework for passenger flow prediction. [[PUB](https://doi.org/10.1007/s10994-025-06795-0)] - FediOS: decoupling orthogonal subspaces for personalization in feature-skew federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06861-7)] - HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06804-2)] - Improve global generalization for personalized federated learning within a Stackelberg game. [[PUB](https://doi.org/10.1007/s10994-025-06770-9)] - TransFed: cross-domain feature alignment for semi-supervised federated transfer learning. [[PUB](https://doi.org/10.1007/s10994-025-06805-1)] - Adaptive collaborative minority oversampling for multi-class imbalanced classification. [[PUB](https://doi.org/10.1007/s10994-025-06899-7)] - Multi-modal co-learning for Earth observation: enhancing single-modality models via modality collaboration. [[PUB](https://doi.org/10.1007/s10994-025-06903-0)] #### UAI - Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication. [[PUB](https://proceedings.mlr.press/v286/zhang25f.html)] - FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning. [[PUB](https://proceedings.mlr.press/v286/tang25a.html)] - FeDCM: Federated Learning of Deep Causal Generative Models. [[PUB](https://proceedings.mlr.press/v286/rahman25a.html)] - Federated Rényi Fair Inference in Federated Heterogeneous System. [[PUB](https://proceedings.mlr.press/v286/ma25a.html)] - FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning. [[PUB](https://proceedings.mlr.press/v286/lin25a.html)] - ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. [[PUB](https://proceedings.mlr.press/v286/karagulyan25a.html)] - FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set. [[PUB](https://proceedings.mlr.press/v286/ibrahim25a.html)] - Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning. [[PUB](https://proceedings.mlr.press/v286/diana25a.html)] - Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information. [[PUB](https://proceedings.mlr.press/v286/akgul25a.html)] - Hindsight Merging: Diverse Data Generation with Language Models. [[PUB](https://proceedings.mlr.press/v286/veselovsky25a.html)] #### ICML - Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off. [[PUB](https://openreview.net/forum?id=C7dmhyTDrx)] [[CODE](https://github.com/6lyc/FedCEO_Collaborate-with-Each-Other)] - Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective. [[PUB](https://openreview.net/forum?id=wleRTUQj07)] - SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding. [[PUB](https://openreview.net/forum?id=j7H4mbeOI1)] [[CODE](https://github.com/NusIoraPrivacy/SecEmb)] - Causality Inspired Federated Learning for OOD Generalization. [[PUB](https://openreview.net/forum?id=pWWUJw2qew)] [[CODE](https://github.com/BIT-DA/CIRL)] - Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging. [[PUB](https://openreview.net/forum?id=HqmXiuFaOr)] [[CODE](https://github.com/junkangLiu0/FedSWA)] - One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models. [[PUB](https://openreview.net/forum?id=PqJFVbJAMR)] [[CODE](https://github.com/HaokunChen245/FedBiP)] - FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models. [[PUB](https://openreview.net/forum?id=XCLZgbm99O)] - An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective. [[PUB](https://openreview.net/forum?id=eLkkXaPFEP)] [[CODE](https://github.com/5Martina5/ESFMC)] - Gap-Dependent Bounds for Federated $Q$-Learning. [[PUB](https://openreview.net/forum?id=0n2nXmOxZS)] - FedBEns: One-Shot Federated Learning based on Bayesian Ensemble. [[PUB](https://openreview.net/forum?id=oTCiv1bkjG)] [[CODE](https://github.com/jacopot96/FedBEns)] - NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel. [[PUB](https://openreview.net/forum?id=hC7zCFk5Dp)] [[CODE](https://github.com/Gabe-Thomp/ntk-dfl)] - Federated Learning for Feature Generalization with Convex Constraints. [[PUB](https://openreview.net/forum?id=pI4AbQ7pg1)] [[CODE](https://github.com/skku-dhkim/FedTorch.git)] - Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos. [[PUB](https://openreview.net/forum?id=EU5lci90fF)] [[CODE](https://github.com/destiny301/uefl)] - Towards Trustworthy Federated Learning with Untrusted Participants. [[PUB](https://openreview.net/forum?id=PjadKnUson)] - Multi-Session Budget Optimization for Forward Auction-based Federated Learning. [[PUB](https://openreview.net/forum?id=bFB0N8ABIr)] - Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation. [[PUB](https://openreview.net/forum?id=0p86Mhg014)] [[CODE](https://github.com/MoratalYang/FedDDA)] - LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation. [[PUB](https://openreview.net/forum?id=li59703WbA)] - Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning. [[PUB](https://openreview.net/forum?id=cgHfR7bt0V)] - Differentially Private Federated $k$-Means Clustering with Server-Side Data. [[PUB](https://openreview.net/forum?id=EFLPHl5RGJ)] [[CODE](https://github.com/jonnyascott/fed-dp-kmeans)] - CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning. [[PUB](https://openreview.net/forum?id=lvkVhZ776k)] - Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees. [[PUB](https://openreview.net/forum?id=MM6ZWF7gl9)] [[CODE](https://github.com/ZLHe0/fedclup)] - $S^2$FGL: Spatial Spectral Federated Graph Learning. [[PUB](https://openreview.net/forum?id=pFQ3MnyIT6)] [[CODE](https://github.com/Wonder7racer/S2FGL.git)] - FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation. [[PUB](https://openreview.net/forum?id=HnwcrtoDd4)] [[CODE](https://github.com/srijith1996/FSL-SAGE)] - Interaction-Aware Gaussian Weighting for Clustered Federated Learning. [[PUB](https://openreview.net/forum?id=dZAQxNFKGg)] [[CODE](https://openreview.net/forum?id=XCLZgbm99O)] - Efficient Heterogeneity-Aware Federated Active Data Selection. [[PUB](https://openreview.net/forum?id=pSdWTED0ZZ)] - Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models. [[PUB](https://openreview.net/forum?id=ny0m8YEUzH)] [[CODE](https://github.com/liaosunny123/FedICU)] - Rethinking the Temperature for Federated Heterogeneous Distillation. [[PUB](https://openreview.net/forum?id=f9xsNQ8oSd)] - FedClean: A General Robust Label Noise Correction for Federated Learning. [[PUB](https://openreview.net/forum?id=4kF2ZZcePc)] - Federated Causal Structure Learning with Non-identical Variable Sets. [[PUB](https://openreview.net/forum?id=QlEx8f3S61)] - FedECADO: A Dynamical System Model of Federated Learning. [[PUB](https://openreview.net/forum?id=gujuGnbhZr)] - Efficient Federated Incomplete Multi-View Clustering. [[PUB](https://openreview.net/forum?id=sylDbssCU9)] [[CODE](https://github.com/Tracesource/EFIMVC)] - Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance. [[PUB](https://openreview.net/forum?id=7qvYLnJDRd)] [[CODE](https://github.com/PaddiHunter/FIMCFG)] - Local Pan-privacy for Federated Analytics. [[PUB](https://openreview.net/forum?id=M18dhHTFf8)] - FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning. [[PUB](https://openreview.net/forum?id=QwTDQXllam)] [[CODE](https://github.com/GanyuWang/FedOne-BDPL)] - Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning. [[PUB](https://openreview.net/forum?id=zV5pkTMHPP)] [[CODE](https://github.com/Hongyao-Chen/HybridBN)] - Private Federated Learning using Preference-Optimized Synthetic Data. [[PUB](https://openreview.net/forum?id=ZuaU2bYzlc)] [[CODE](https://github.com/meiyuw/POPri)] - Enhancing Foundation Models with Federated Domain Knowledge Infusion. [[PUB](https://openreview.net/forum?id=6SIVFmjIm4)] - FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation. [[PUB](https://openreview.net/forum?id=y7pDvbi9xz)] [[CODE](https://github.com/CYFang6/FedPHA)] - Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework. [[PUB](https://openreview.net/forum?id=jwjvkWsePB)] [[CODE](https://app.box.com/s/phf6bhjy6owcr6b1rvfe412fiw059pxk)] - Federated Node-Level Clustering Network with Cross-Subgraph Link Mending. [[PUB](https://openreview.net/forum?id=38Nh0TebXZ)] - Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. [[PUB](https://openreview.net/forum?id=mzPArjGqrs)] [[CODE](https://github.com/allen4747/Ferret)] - FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence. [[PUB](https://openreview.net/forum?id=9hFQvmCl7P)] - Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework. [[PUB](https://openreview.net/forum?id=M7mVzCV6uU)] [[CODE](https://github.com/Terje-M/FedGVI)] - DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence. [[PUB](https://openreview.net/forum?id=EvzArsKUww)] - On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists. [[PUB](https://openreview.net/forum?id=Eog0kXX7hW)] - Safe-EF: Error Feedback for Non-smooth Constrained Optimization. [[PUB](https://openreview.net/forum?id=9D5aM5LQ3Y)] [[CODE](https://github.com/yardenas/safe-ef)] - Gradient Inversion of Multimodal Models. [[PUB](https://openreview.net/forum?id=j4IELrBhoG)] [[CODE](https://github.com/AlonZolfi/gi-dqa)] - Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg. [[PUB](https://openreview.net/forum?id=0p04srg7uf)] [[CODE](https://github.com/kkhuge/ICML2025)] - Decoupled SGDA for Games with Intermittent Strategy Communication. [[PUB](https://openreview.net/forum?id=ZYkFTSEZ6k)] - Private Model Personalization Revisited. [[PUB](https://openreview.net/forum?id=hw1kGPcSZ5)] - Leveraging Randomness in Model and Data Partitioning for Privacy Amplification. [[PUB](https://openreview.net/forum?id=3K6BkFZ7ka)] - Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. [[PUB](https://openreview.net/forum?id=2XvOJvUlKc)] [[CODE](https://github.com/pmangold/scaffold-speed-up)] - Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning. [[PUB](https://openreview.net/forum?id=hrBfufwMzg)] [[CODE](https://github.com/buptcmm/phnhvvs)] - FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates. [[PUB](https://openreview.net/forum?id=V18WOxHRMq)] [[CODE](https://github.com/lxy66888/fedsmu.git)] - One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory. [[PUB](https://openreview.net/forum?id=80mK2Mqaph)] [[CODE](https://github.com/harrylee999/FL-SAM)] - Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts. [[PUB](https://openreview.net/forum?id=dKfq3JbjnE)] - Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning. [[PUB](https://openreview.net/forum?id=2XvF67vbCK)] [[CODE](https://github.com/zenghui9977/FAFI_ICML25)] - GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention. [[PUB](https://openreview.net/forum?id=nAk0ENu8LS)] [[CODE](https://github.com/JiaruQian/GHOST)] - DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing. [[PUB](https://openreview.net/forum?id=Nv6mOSqUVA)] - BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach. [[PUB](https://openreview.net/forum?id=fmlol78Qqf)] - Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning. [[PUB](https://openreview.net/forum?id=HdS6tZwwa7)] - EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification. [[PUB](https://openreview.net/forum?id=Bd9JlrqZhN)] [[CODE](https://github.com/ZitongShi/EAGLES)] - Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference. [[PUB](https://openreview.net/forum?id=Zn6hmmBnAa)] [[CODE](https://github.com/mahendrathapa/pFedHB)] - Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models. [[PUB](https://openreview.net/forum?id=R7gCixl2xR)] [[CODE](https://github.com/GivralNguyen/FL-LDP-AMI)] - Generalization in Federated Learning: A Conditional Mutual Information Framework. [[PUB](https://openreview.net/forum?id=kOttDCDYJp)] - The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=aooq3tQIX9)] [[CODE](https://github.com/Leopold1423/fedmud-icml25)] - Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions. [[PUB](https://openreview.net/forum?id=rCJNbDXkvC)] [[CODE](https://github.com/dcll-iiitd/CoresetForVFL)] - Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. [[PUB](https://openreview.net/forum?id=ULZHqJU4ZC)] - Federated In-Context Learning: Iterative Refinement for Improved Answer Quality. [[PUB](https://openreview.net/forum?id=TUk7gCqtmf)] - SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution. [[PUB](https://openreview.net/forum?id=Kjz03pmyW0)] [[CODE](https://github.com/WenddHe0119/SPMC)] - You Get What You Give: Reciprocally Fair Federated Learning. [[PUB](https://openreview.net/forum?id=ZdmMDz33Io)] - Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead. [[PUB](https://openreview.net/forum?id=6znPjYn11w)] [[CODE](https://github.com/pupiu45/FedGO)] - Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning. [[PUB](https://openreview.net/forum?id=iBOMvaa2aN)] - Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts. [[PUB](https://proceedings.mlr.press/v267/li25eb.html)] - Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks. [[PUB](https://proceedings.mlr.press/v267/tastan25a.html)] - BECAME: Bayesian Continual Learning with Adaptive Model Merging. [[PUB](https://proceedings.mlr.press/v267/li25bk.html)] - BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly. [[PUB](https://proceedings.mlr.press/v267/shen25i.html)] - Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging. [[PUB](https://proceedings.mlr.press/v267/chen25cm.html)] - CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging. [[PUB](https://proceedings.mlr.press/v267/yang25x.html)] - CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging. [[PUB](https://proceedings.mlr.press/v267/sun25i.html)] - Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold. [[PUB](https://proceedings.mlr.press/v267/song25c.html)] - Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. [[PUB](https://proceedings.mlr.press/v267/gotz25a.html)] - HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training. [[PUB](https://proceedings.mlr.press/v267/kim25y.html)] - Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent. [[PUB](https://proceedings.mlr.press/v267/wei25k.html)] - Mutual Learning for SAM Adaptation: A Dual Collaborative Network Framework for Source-Free Domain Transfer. [[PUB](https://proceedings.mlr.press/v267/liu25ca.html)] - No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces. [[PUB](https://proceedings.mlr.press/v267/marczak25a.html)] [[CODE](https://github.com/danielm1405/iso-merging)] - Pareto Merging: Multi-Objective Optimization for Preference-Aware Model Merging. [[PUB](https://proceedings.mlr.press/v267/chen25af.html)] - Representation Surgery in Model Merging with Probabilistic Modeling. [[PUB](https://proceedings.mlr.press/v267/wei25c.html)] - Scalable Model Merging with Progressive Layer-wise Distillation. [[PUB](https://proceedings.mlr.press/v267/xu25r.html)] - ToMA: Token Merge with Attention for Diffusion Models. [[PUB](https://proceedings.mlr.press/v267/lu25v.html)] [[CODE](https://github.com/WenboLuu/ToMA)] - Whoever Started the interference Should End It: Guiding Data-Free Model Merging via Task Vectors. [[PUB](https://proceedings.mlr.press/v267/cheng25h.html)] #### Mach Learn - HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06804-2)] - Fedflow: a personalized federated learning framework for passenger flow prediction. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06795-0)] - Federated causal inference from observational data. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06819-9)] - TransFed: cross-domain feature alignment for semi-supervised federated transfer learning. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06805-1)] - Improve global generalization for personalized federated learning within a Stackelberg game. [[PUB](https://link.springer.com/article/10.1007/s10994-025-06770-9)] - Efficient federated unlearning under plausible deniability. [[PUB](https://link.springer.com/article/10.1007/s10994-024-06685-x)] [[CODE](https://github.com/Ayush-Umu/Federated-Unlearning-under-Plausible-Deniability)] - Auction-based incentive mechanism with personalized privacy protection in federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06836-8)] - DP-FedSecure: a secure and efficient federated learning scheme based on adaptive differential privacy. [[PUB](https://doi.org/10.1007/s10994-025-06888-w)] - FediOS: decoupling orthogonal subspaces for personalization in feature-skew federated learning. [[PUB](https://doi.org/10.1007/s10994-025-06861-7)] #### ICLR - Energy-based Backdoor Defense Against Federated Graph Learning. [[PUB](https://openreview.net/forum?id=5Jc7r5aqHJ)] - DEPT: Decoupled Embeddings for Pre-training Language Models. [[PUB](https://openreview.net/forum?id=vf5aUZT0Fz)] - Subgraph Federated Learning for Local Generalization. [[PUB](https://openreview.net/forum?id=cH65nS5sOz)] [[CODE](https://github.com/sung-won-kim/FedLoG)] - Problem-Parameter-Free Federated Learning. [[PUB](https://openreview.net/forum?id=ZuazHmXTns)] - Adaptive Gradient Clipping for Robust Federated Learning. [[PUB](https://openreview.net/forum?id=03OkC0LKDD)] - Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees. [[PUB](https://openreview.net/forum?id=cznqgb4DNv)] - LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression. [[PUB](https://openreview.net/forum?id=PpYy0dR3Qw)] - Group Distributionally Robust Dataset Distillation with Risk Minimization. [[PUB](https://openreview.net/forum?id=3JsU5QXNru)] - GRAIN: Exact Graph Reconstruction from Gradients. [[PUB](https://openreview.net/forum?id=7bAjVh3CG3)] - Towards Faster Decentralized Stochastic Optimization with Communication Compression. [[PUB](https://openreview.net/forum?id=CMMpcs9prj)] - Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization. [[PUB](https://openreview.net/forum?id=Bl3e8HV9xW)] - Many-Objective Multi-Solution Transport. [[PUB](https://openreview.net/forum?id=Neb17mimVH)] - Query-based Knowledge Transfer for Heterogeneous Learning Environments. [[PUB](https://openreview.net/forum?id=XKv29sMyjF)] - Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting. [[PUB](https://openreview.net/forum?id=ydREOIttdC)] - Federated Granger Causality Learning For Interdependent Clients With State Space Representation. [[PUB](https://openreview.net/forum?id=KTgQGXz5xj)] - Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization. [[PUB](https://openreview.net/forum?id=omrLHFzC37)] - Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. [[PUB](https://openreview.net/forum?id=TrJ36UfD9P)] - On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=7pDI74iOyu)] - PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models. [[PUB](https://openreview.net/forum?id=B9kUJuWrYC)] - Differentially Private Federated Learning with Time-Adaptive Privacy Spending. [[PUB](https://openreview.net/forum?id=W0nydevOlG)] - Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies. [[PUB](https://openreview.net/forum?id=zPDpdk3V8L)] - Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis. [[PUB](https://openreview.net/forum?id=5DUekOKWcS)] - On the Byzantine-Resilience of Distillation-Based Federated Learning. [[PUB](https://openreview.net/forum?id=of6EuHT7de)] - Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models. [[PUB](https://openreview.net/forum?id=sYNWqQYJhz)] - Event-Driven Online Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=FCBbh0HCrF)] - On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations. [[PUB](https://openreview.net/forum?id=BfUDZGqCAu)] - Federated Domain Generalization with Data-free On-server Matching Gradient. [[PUB](https://openreview.net/forum?id=8TERgu1Lb2)] - Unlocking the Potential of Model Calibration in Federated Learning. [[PUB](https://openreview.net/forum?id=Osr0KZJeTX)] - FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking. [[PUB](https://openreview.net/forum?id=6RjQ54M1rM)] - Understanding the Stability-based Generalization of Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=znhZbonEoe)] - Federated Residual Low-Rank Adaption of Large Language Models. [[PUB](https://openreview.net/forum?id=e0rQRMUhs7)] - FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine. [[PUB](https://openreview.net/forum?id=44hcrfzydU)] - Vertical Federated Learning with Missing Features During Training and Inference. [[PUB](https://openreview.net/forum?id=OXi1FmHGzz)] [[CODE](https://github.com/Valdeira/LASER-VFL)] - Federated $Q$-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost. [[PUB](https://openreview.net/forum?id=FoUpv84hMw)] - Selective Aggregation for Low-Rank Adaptation in Federated Learning. [[PUB](https://openreview.net/forum?id=iX3uESGdsO)] [[CODE](https://github.com/Pengxin-Guo/FedSA-LoRA)] - Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models. [[PUB](https://openreview.net/forum?id=Equ277PBN0)] - Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection. [[PUB](https://openreview.net/forum?id=B8akWa62Da)] - Debiasing Federated Learning with Correlated Client Participation. [[PUB](https://openreview.net/forum?id=9h45qxXEx0)] - Decoupled Subgraph Federated Learning. [[PUB](https://openreview.net/forum?id=v1rFkElnIn)] - Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=79nO2DPjVX)] - Towards Federated RLHF with Aggregated Client Preference for LLMs. [[PUB](https://openreview.net/forum?id=mqNKiEB6pd)] - SparsyFed: Sparse Adaptive Federated Learning. [[PUB](https://openreview.net/forum?id=OBUQNASaWw)] - Can Textual Gradient Work in Federated Learning?. [[PUB](https://openreview.net/forum?id=Cy5IKvYbR3)] - Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models. [[PUB](https://openreview.net/forum?id=xiDJaTim3P)] [[CODE](https://github.com/ljaiverson/pFedMoAP)] - Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning. [[PUB](https://openreview.net/forum?id=3wEGdrV5Cb)] - Connecting Federated ADMM to Bayes. [[PUB](https://openreview.net/forum?id=ipQrjRsl11)] - Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning. [[PUB](https://openreview.net/forum?id=ROpY0qRUXL)] - Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond. [[PUB](https://openreview.net/forum?id=f65RuQgVlp)] - Federated Few-Shot Class-Incremental Learning. [[PUB](https://openreview.net/forum?id=ZiPoAlKf9Y)] - Federated Residual Low-Rank Adaptation of Large Language Models. [[PUB](https://openreview.net/forum?id=e0rQRMUhs7)] - Collaborative Discrete-Continuous Black-Box Prompt Learning for Language Models. [[PUB](https://openreview.net/forum?id=sdLGY9Dj5r)] - EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models. [[PUB](https://openreview.net/forum?id=xtlMtbVfWu)] - LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging. [[PUB](https://openreview.net/forum?id=J5sUOvlLbQ)] [[CODE](https://github.com/wang-kee/LiNeS)] - MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation. [[PUB](https://openreview.net/forum?id=1v7SRWsYve)] - Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning. [[PUB](https://openreview.net/forum?id=eaTqsptDPL)] [[CODE](https://github.com/baiklab/SAFT-Merge)] - Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace. [[PUB](https://openreview.net/forum?id=dqMqAaw7Sq)] [[CODE](https://github.com/Yangjinluan/DAM)] - Model merging with SVD to tie the Knots. [[PUB](https://openreview.net/forum?id=67X93aZHII)] [[CODE](https://github.com/gstoica27/KnOTS)] - MrT5: Dynamic Token Merging for Efficient Byte-level Language Models. [[PUB](https://openreview.net/forum?id=VYWBMq1L7H)] - Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine. [[PUB](https://openreview.net/forum?id=pH543jrbe8)] - REMEDY: Recipe Merging Dynamics in Large Vision-Language Models. [[PUB](https://openreview.net/forum?id=iX7eHHE5Tx)] - Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models. [[PUB](https://openreview.net/forum?id=mQ55y4s5hj)] #### TPAMI - DFedADMM: Dual Constraint Controlled Model Inconsistency for Decentralize Federated Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3546659)] - Federated Multi-View K-Means Clustering. [[PUB](https://doi.org/10.1109/TPAMI.2024.3520708)] - FedID: Enhancing Federated Learning Security Through Dynamic Identification. [[PUB](https://doi.org/10.1109/TPAMI.2025.3581555)] - Medical Federated Model With Mixture of Personalized and Shared Components. [[PUB](https://doi.org/10.1109/TPAMI.2024.3470072)] - Re-Fed+: A Better Replay Strategy for Federated Incremental Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3551732)] - Robust Asymmetric Heterogeneous Federated Learning With Corrupted Clients. [[PUB](https://doi.org/10.1109/TPAMI.2025.3527137)] - Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation. [[PUB](https://doi.org/10.1109/TPAMI.2024.3469188)] - Toward the Flatter Landscape and Better Generalization in Federated Learning Under Client-Level Differential Privacy. [[PUB](https://doi.org/10.1109/TPAMI.2025.3597922)] - VQ-FedDiff: Federated Learning Algorithm of Diffusion Models With Client-Specific Vector-Quantized Conditioning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3602282)] - NICE: Improving Panoptic Narrative Detection and Segmentation With Cascading Collaborative Learning. [[PUB](https://doi.org/10.1109/TPAMI.2025.3583795)] ### 2024 #### colt - The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. [[PUB](https://proceedings.mlr.press/v247/patel24a.html)] #### machine learning - Aligning model outputs for class imbalanced non-IID federated learning. [[PUB](https://doi.org/10.1007/s10994-022-06241-5)] - Communication-efficient clustered federated learning via model distance. [[PUB](https://doi.org/10.1007/s10994-023-06443-5)] - Federated learning with superquantile aggregation for heterogeneous data. [[PUB](https://doi.org/10.1007/s10994-023-06332-x)] - Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization. [[PUB](https://doi.org/10.1007/s10994-024-06541-y)] #### UAI - FedAST: Federated Asynchronous Simultaneous Training. [[PUB](https://proceedings.mlr.press/v244/askin24a.html)] - On Convergence of Federated Averaging Langevin Dynamics. [[PUB](https://proceedings.mlr.press/v244/deng24a.html)] - On the Convergence of Hierarchical Federated Learning with Partial Worker Participation. [[PUB](https://proceedings.mlr.press/v244/jiang24a.html)] - Pure Exploration in Asynchronous Federated Bandits. [[PUB](https://proceedings.mlr.press/v244/wang24c.html)] #### NeurIPS - One-shot Federated Learning via Synthetic Distiller-Distillate Communication. [[PUB](https://openreview.net/forum?id=6292sp7HiE)] - Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data. [[PUB](https://openreview.net/forum?id=uO53206oLJ)] - FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation. [[PUB](https://openreview.net/forum?id=c3OZBJpN7M)] - Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach. [[PUB](https://openreview.net/forum?id=6lx34fpanw)] - Federated Model Heterogeneous Matryoshka Representation Learning. [[PUB](https://openreview.net/forum?id=5yboFMpvHf)] - Federated Graph Learning for Cross-Domain Recommendation. [[PUB](https://openreview.net/forum?id=UBpPOqrBKE)] - FedGMark: Certifiably Robust Watermarking for Federated Graph Learning. [[PUB](https://openreview.net/forum?id=xeviQPXTMU)] - Dual-Personalizing Adapter for Federated Foundation Models. [[PUB](https://openreview.net/forum?id=nkwPiBSw1f)] - Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning. [[PUB](https://openreview.net/forum?id=DUFD6vsyF8)] - Taming the Long Tail in Human Mobility Prediction. [[PUB](https://openreview.net/forum?id=wT2TIfHKp8)] - Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning. [[PUB](https://openreview.net/forum?id=EVw8Jh5Et9)] - Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution. [[PUB](https://openreview.net/forum?id=55zLbH7dE1)] - DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting. [[PUB](https://openreview.net/forum?id=FDfrPugkGU)] - Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability. [[PUB](https://openreview.net/forum?id=DLNOBJa7TM)] - Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data. [[PUB](https://openreview.net/forum?id=FqWyzyErVT)] - FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction. [[PUB](https://openreview.net/forum?id=bMbteQRhDI)] - Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. [[PUB](https://openreview.net/forum?id=nw6ANsC66G)] - FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations. [[PUB](https://openreview.net/forum?id=TcCorXxNJQ)] [[CODE](https://github.com/ATP-1010/FederatedLLM)] - Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains. [[PUB](https://openreview.net/forum?id=6SRPizFuaE)] - pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=xW6ga9i4eA)] - Why Go Full? Elevating Federated Learning Through Partial Network Updates. [[PUB](https://openreview.net/forum?id=6OK8Qy9yVu)] - FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. [[PUB](https://openreview.net/forum?id=E7fZOoiEKl)] - FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference. [[PUB](https://openreview.net/forum?id=I96GFYalFO)] - Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation. [[PUB](https://openreview.net/forum?id=3YIyB82rjX)] - A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs. [[PUB](https://openreview.net/forum?id=h1iMVi2iEM)] - Private and Personalized Frequency Estimation in a Federated Setting. [[PUB](https://openreview.net/forum?id=0nzKznCjFG)] - The Sample-Communication Complexity Trade-off in Federated Q-Learning. [[PUB](https://openreview.net/forum?id=6YIpvnkjUK)] - Federated Ensemble-Directed Offline Reinforcement Learning. [[PUB](https://openreview.net/forum?id=ypaqE8UwsC)] - Federated Black-Box Adaptation for Semantic Segmentation. [[PUB](https://openreview.net/forum?id=Fp3JVz5XE7)] - Thinking Forward: Memory-Efficient Federated Finetuning of Language Models. [[PUB](https://openreview.net/forum?id=dGQtja9X2C)] [[CODE](https://github.com/Astuary/Spry)] - Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. [[PUB](https://openreview.net/forum?id=Y4L8GQXZZO)] - Optimal Design for Human Preference Elicitation. [[PUB](https://openreview.net/forum?id=cCGWj61Ael)] - Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration. [[PUB](https://openreview.net/forum?id=y6JotynERr)] - Personalized Federated Learning via Feature Distribution Adaptation. [[PUB](https://openreview.net/forum?id=Wl2optQcng)] - SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. [[PUB](https://openreview.net/forum?id=HeJ1cBAgiV)] - A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings. [[PUB](https://openreview.net/forum?id=hilGwNabqB)] - RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation. [[PUB](https://openreview.net/forum?id=js74ZCddxG)] - FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning. [[PUB](https://openreview.net/forum?id=QXkFC7D6p4)] - End-to-end Learnable Clustering for Intent Learning in Recommendation. [[PUB](https://openreview.net/forum?id=As91fJvY9E)] - FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation. [[PUB](https://openreview.net/forum?id=I3IuclVLFZ)] - Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting. [[PUB](https://openreview.net/forum?id=HS0faHRhWD)] - FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection. [[PUB](https://openreview.net/forum?id=D6MQrw9HFu)] [[CODE](https://github.com/XeniaLLL/FOOGD-main.git)] - A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm. [[PUB](https://openreview.net/forum?id=3YkeHuT1o6)] - FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction. [[PUB](https://openreview.net/forum?id=zBMKodNgKX)] - Low Precision Local Training is Enough for Federated Learning. [[PUB](https://openreview.net/forum?id=vvpewjtnvm)] [[CODE](https://github.com/digbangbang/LPT-FL)] - Resource-Aware Federated Self-Supervised Learning with Global Class Representations. [[PUB](https://openreview.net/forum?id=Of4iNAIUSe)] - On the Necessity of Collaboration for Online Model Selection with Decentralized Data. [[PUB](https://openreview.net/forum?id=uqWfLgZpV1)] - The Power of Extrapolation in Federated Learning. [[PUB](https://openreview.net/forum?id=FuTfZK7PK3)] - (FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning. [[PUB](https://openreview.net/forum?id=lflwtGE6Vf)] - On Sampling Strategies for Spectral Model Sharding. [[PUB](https://openreview.net/forum?id=PgTHgLUFi3)] - Customizing Language Models with Instance-wise LoRA for Sequential Recommendation. [[PUB](https://openreview.net/forum?id=isZ8XRe3De)] - SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead. [[PUB](https://openreview.net/forum?id=dAXuir2ets)] - HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning. [[PUB](https://openreview.net/forum?id=6LVxO1C819)] - Stabilized Proximal-Point Methods for Federated Optimization. [[PUB](https://openreview.net/forum?id=WukSyFSzDt)] - DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices. [[PUB](https://openreview.net/forum?id=Pezt0xttae)] - Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=g8wnC1E1OS)] - Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD. [[PUB](https://openreview.net/forum?id=j6Zsoj544N)] - FedAvP: Augment Local Data via Shared Policy in Federated Learning. [[PUB](https://openreview.net/forum?id=M1PRU0x1Iz)] - CoBo: Collaborative Learning via Bilevel Optimization. [[PUB](https://openreview.net/forum?id=SjQ1iIqpfU)] - Convergence Analysis of Split Federated Learning on Heterogeneous Data. [[PUB](https://openreview.net/forum?id=ud0RBkdBfE)] - Communication-Efficient Federated Group Distributionally Robust Optimization. [[PUB](https://openreview.net/forum?id=xNZEjFe0mh)] - Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity. [[PUB](https://openreview.net/forum?id=YxyYTcv3hp)] [[CODE](https://github.com/OngWinKent/Federated-Feature-Unlearning)] - Federated Learning over Connected Modes. [[PUB](https://openreview.net/forum?id=JL2eMCfDW8)] - Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning. [[PUB](https://openreview.net/forum?id=yvUHnBkCzd)] - Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors. [[PUB](https://openreview.net/forum?id=1kyc4TSOFZ)] - Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups. [[PUB](https://openreview.net/forum?id=T826pwZLci)] - DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans. [[PUB](https://openreview.net/forum?id=792txRlKit)] [[CODE](https://github.com/yuangan/DataStealing)] - Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning. [[PUB](https://openreview.net/forum?id=5FHzrRGOKR)] - Hierarchical Federated Learning with Multi-Timescale Gradient Correction. [[PUB](https://openreview.net/forum?id=aCAb1qNXI0)] - HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links. [[PUB](https://openreview.net/forum?id=3ie8NWA1El)] - SPEAR: Exact Gradient Inversion of Batches in Federated Learning. [[PUB](https://openreview.net/forum?id=lPDxPVS6ix)] - Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis. [[PUB](https://openreview.net/forum?id=WftaVkL6G2)] - Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views. [[PUB](https://openreview.net/forum?id=GVlJVX3iiq)] [[CODE](https://github.com/5Martina5/FMCSC)] - Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data. [[PUB](https://openreview.net/forum?id=G89r8Mgi5r)] - Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning. [[PUB](https://openreview.net/forum?id=0LfgE6kvKZ)] - Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning. [[PUB](https://openreview.net/forum?id=MwJo3zuiTm)] - Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift. [[PUB](https://openreview.net/forum?id=6ejpSVIiIl)] [[CODE](https://github.com/Chen-Junbao/FedCCFA)] - Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning. [[PUB](https://openreview.net/forum?id=HhnpPISAUH)] - FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?. [[PUB](https://openreview.net/forum?id=JiRGxrqHh0)] - Active preference learning for ordering items in- and out-of-sample. [[PUB](https://openreview.net/forum?id=PSLH5q7PFo)] - Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources. [[PUB](https://openreview.net/forum?id=gkOzoHBXUw)] - Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients. [[PUB](https://openreview.net/forum?id=WBLPlszJI5)] - Revisiting Ensembling in One-Shot Federated Learning. [[PUB](https://openreview.net/forum?id=7rWTS2wuYX)] - FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models. [[PUB](https://openreview.net/forum?id=djGx0hucok)] - $ exttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning. [[PUB](https://openreview.net/forum?id=I79q7wIRkS)] - FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection. [[PUB](https://openreview.net/forum?id=rovpCs3ZEO)] - pfl-research: simulation framework for accelerating research in Private Federated Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/4c8c6de56ecdd05e61abcd9e057c6142-Abstract-Datasets_and_Benchmarks_Track.html)] - $C2M3$: Cycle-Consistent Multi-Model Merging. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/3268f1e2474ef9d1af7f034401197a7f-Abstract-Conference.html)] - A Kernel Perspective on Distillation-based Collaborative Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/a71c1931d3fb8ba564f7458d0657d0b1-Abstract-Conference.html)] - Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/013f9cd52b38e3e53475605d2b8e7c23-Abstract-Conference.html)] [[CODE](https://github.com/bigdata-ustc/Coral)] - Collaborative Refining for Learning from Inaccurate Labels. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/a8809ae67a7aad49a64d615468d72808-Abstract-Conference.html)] - Communication Efficient Distributed Training with Distributed Lion. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/20cea6c1b36ae5f69c48427a68b67fbc-Abstract-Conference.html)] - DAGER: Exact Gradient Inversion for Large Language Models. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/9ff1577a1f8308df1ccea6b4f64a103f-Abstract-Conference.html)] - EMR-Merging: Tuning-Free High-Performance Model Merging. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/dda5cac5272a9bcd4bc73d90bc725ef1-Abstract-Conference.html)] - Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/d8a6eb79f8ccaacbe7198a5caf3a0323-Abstract-Conference.html)] - Gradient-free Decoder Inversion in Latent Diffusion Models. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/970f59b22f4c72aec75174aae63c7459-Abstract-Conference.html)] - Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/b041cbfcc3f282a9b3c8eb9c16177529-Abstract-Conference.html)] - Parameter Competition Balancing for Model Merging. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/99fc8bc48b917c301a80cb74d91c0c06-Abstract-Conference.html)] - SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/a97b58c4f7551053b0512f92244b0810-Abstract-Conference.html)] - Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/8fcd17eb91bae20d9826786d7d6be799-Abstract-Conference.html)] - Unravelling in Collaborative Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2024/hash/b0499a1aecf036d42074d03f621d7864-Abstract-Conference.html)] #### NeurIPS workshop - Momentum Approximation in Asynchronous Private Federated Learning. [[PUB](https://openreview.net/forum?id=pEpjKicxFk)] - Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning. [[PUB](https://openreview.net/forum?id=8TrYvsbw1f)] - Federated Learning with Generative Content. [[PUB](https://openreview.net/forum?id=hMbgXHjWrg)] - Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models. [[PUB](https://openreview.net/forum?id=pxP2M3xiE6)] - Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models. [[PUB](https://openreview.net/forum?id=1JGa1OIRjQ)] - Defection-Free Collaboration between Competitors in a Learning System. [[PUB](https://openreview.net/forum?id=2Sd5xNv1sm)] - On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments. [[PUB](https://openreview.net/forum?id=Eph8dS188u)] - EncCluster: Bringing Functional Encryption in Federated Foundational Models. [[PUB](https://openreview.net/forum?id=7bgJ7t5kkW)] - Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. [[PUB](https://openreview.net/forum?id=SXMsg44Znz)] - Hot Pluggable Federated Learning. [[PUB](https://openreview.net/forum?id=FazIrAXoM6)] - Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees. [[PUB](https://openreview.net/forum?id=MxgmAil8ud)] - The Future of Large Language Model Pre-training is Federated. [[PUB](https://openreview.net/forum?id=hfeH5AP9NY)] - Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms. [[PUB](https://openreview.net/forum?id=jNZEIQsJes)] - The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain. [[PUB](https://openreview.net/forum?id=RoUUV2wLdn)] - ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=zpEQUbYZPc)] - DeComFL: Federated Learning with Dimension-Free Communication. [[PUB](https://openreview.net/forum?id=Vy9ltlTXXd)] - Improving Group Connectivity for Generalization of Federated Deep Learning. [[PUB](https://openreview.net/forum?id=vGyB8PVl4C)] - MAP: Model Merging with Amortized Pareto Front Using Limited Computation. [[PUB](https://openreview.net/forum?id=KfOdVp4pfm)] - OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning. [[PUB](https://openreview.net/forum?id=qQdPSuW7qx)] - Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration. [[PUB](https://openreview.net/forum?id=OxpWu6J0TW)] - Worldwide Federated Training of Language Models. [[PUB](https://openreview.net/forum?id=YMSLZUmQVV)] - FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator. [[PUB](https://openreview.net/forum?id=uBooD9HQQu)] - Enhancing Causal Discovery in Federated Settings with Limited Local Samples. [[PUB](https://openreview.net/forum?id=Js64okXDUE)] - $ exttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning. [[PUB](https://openreview.net/forum?id=6WNNB9TaVw)] - DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing. [[PUB](https://openreview.net/forum?id=GdzTE7eruH)] #### JMLR - FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization. [[PUB](https://jmlr.org/papers/v25/23-0764.html)] - A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness. [[PUB](https://jmlr.org/papers/v25/23-0215.html)] - Compressed and distributed least-squares regression: convergence rates with applications to federated learning. [[PUB](https://jmlr.org/papers/v25/23-1040.html)] - Countering the Communication Bottleneck in Federated Learning: A Highly Efficient Zero-Order Optimization Technique. [[PUB](https://jmlr.org/papers/v25/24-1189.html)] - Federated Automatic Differentiation. [[PUB](https://jmlr.org/papers/v25/23-0223.html)] - Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning. [[PUB](https://jmlr.org/papers/v25/22-1036.html)] - Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms. [[PUB](https://jmlr.org/papers/v25/21-0316.html)] #### ICML - Effective Federated Graph Matching. [[PUB](https://openreview.net/forum?id=rSfzchjIYu)] - Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation. [[PUB](https://openreview.net/forum?id=zwUEk9WpsR)] - Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients. [[PUB](https://openreview.net/forum?id=2zLt2Odckx)] - FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models. [[PUB](https://openreview.net/forum?id=AoYhtJ4A90)] - Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning. [[PUB](https://openreview.net/forum?id=p0MGN0LSnx)] - A New Theoretical Perspective on Data Heterogeneity in Federated Optimization. [[PUB](https://openreview.net/forum?id=re6es2atbl)] - Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models. [[](https://openreview.net/forum?id=QgvBcOsF4B)] - Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments. [[PUB](https://openreview.net/forum?id=g43yUNWX4V)] - Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates. [[PUB](https://openreview.net/forum?id=Izv7gBnap3)] - Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective. [[PUB](https://openreview.net/forum?id=yHRxnhKyEJ)] - Accelerating Federated Learning with Quick Distributed Mean Estimation. [[PUB](https://openreview.net/forum?id=gWEwIlZrbQ)] - Fair Federated Learning via the Proportional Veto Core. [[PUB](https://openreview.net/forum?id=6Zgjrowepn)] - AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning. [[PUB](https://openreview.net/forum?id=PHUAG63Efe)] [[CODE](https://github.com/MIC-DKFZ/deki-smpc)] - Recovering Labels from Local Updates in Federated Learning. [[PUB](https://openreview.net/forum?id=E41gvBG4s6)] - FedMBridge: Bridgeable Multimodal Federated Learning. [[PUB](https://openreview.net/forum?id=jrHUbftLd6)] - Harmonizing Generalization and Personalization in Federated Prompt Learning. [[PUB](https://openreview.net/forum?id=YYwERRXsJW)] - Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. [[PUB](https://openreview.net/forum?id=6axTFAlzRV)] - Accelerating Heterogeneous Federated Learning with Closed-form Classifiers. [[PUB](https://openreview.net/forum?id=cMige5MK1N)] - Federated Combinatorial Multi-Agent Multi-Armed Bandits. [[PUB](https://openreview.net/forum?id=lrFwPeDdEQ)] - A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization. [[PUB](https://openreview.net/forum?id=GentO2E4ID)] - Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses. [[PUB](https://openreview.net/forum?id=sSAEhcdB9N)] - FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering. [[PUB](https://openreview.net/forum?id=kc4dZYJlJG)] - Pursuing Overall Welfare in Federated Learning through Sequential Decision Making. [[PUB](https://openreview.net/forum?id=foPMkomvk1)] [[CODE](https://github.com/vaseline555/AAggFF)] - PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. [[PUB](https://openreview.net/forum?id=3WCvnkHnxV)] [[CODE](https://github.com/houcharlie/PrE-Text)] - Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=k2axqNsVVO)] - Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors. [[PUB](https://openreview.net/forum?id=mNzkumTSVL)] - Federated Optimization with Doubly Regularized Drift Correction. [[PUB](https://openreview.net/forum?id=JD03zxWZzs)] - FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data. [[PUB](https://openreview.net/forum?id=0nMzOmkBHC)] - Certifiably Byzantine-Robust Federated Conformal Prediction. [[PUB](https://openreview.net/forum?id=4axAQHwBOE)] - Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning. [[PUB](https://openreview.net/forum?id=vQmVmMN5ft)] - Clustered Federated Learning via Gradient-based Partitioning. [[PUB](https://openreview.net/forum?id=J4HJUF70qm)] - Recurrent Early Exits for Federated Learning with Heterogeneous Clients. [[PUB](https://openreview.net/forum?id=w4B42sxNq3)] - Rethinking the Flat Minima Searching in Federated Learning. [[PUB](https://openreview.net/forum?id=6TM62kpI5c)] - FedBAT: Communication-Efficient Federated Learning via Learnable Binarization. [[PUB](https://openreview.net/forum?id=x2zxPwCkAZ)] - Federated Representation Learning in the Under-Parameterized Regime. [[PUB](https://openreview.net/forum?id=LIQYhV45D4)] - FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees. [[PUB](https://openreview.net/forum?id=akyElNlUVA)] - Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. [[PUB](https://openreview.net/forum?id=wuQ2DRPAuy)] - SILVER: Single-loop variance reduction and application to federated learning. [[PUB](https://openreview.net/forum?id=pOgMluzEIH)] - SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding. [[PUB](https://openreview.net/forum?id=zEqeNEuiJr)] - FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler. [[PUB](https://openreview.net/forum?id=XecUTmB9yD)] - Federated Continual Learning via Prompt-based Dual Knowledge Transfer. [[PUB](https://openreview.net/forum?id=Kqa5JakTjB)] - Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes. [[PUB](https://openreview.net/forum?id=cit0hg4sEz)] - Decomposable Submodular Maximization in Federated Setting. [[PUB](https://openreview.net/forum?id=SAbZExIIgG)] - Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. [[PUB](https://openreview.net/forum?id=sTVSyqD6XX)] - Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials. [[PUB](https://openreview.net/forum?id=01M0N8VgfB)] - Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!. [[PUB](https://openreview.net/forum?id=ffS0aYP6mk)] - Byzantine Resilient and Fast Federated Few-Shot Learning. [[PUB](https://openreview.net/forum?id=q5q59s2WJy)] - Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization. [[PUB](https://openreview.net/forum?id=Kbd9A4lVoX)] - Ranking-based Client Imitation Selection for Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=FMEhnS0948)] - Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms. [[PUB](https://openreview.net/forum?id=kVgpa1rfLO)] - FADAS: Towards Federated Adaptive Asynchronous Optimization. [[PUB](https://openreview.net/forum?id=j56JAd29uH)] - Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices. [[PUB](https://openreview.net/forum?id=LIPGadocTe)] - FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error. [[PUB](https://openreview.net/forum?id=Wjq2bS7fTK)] - MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis. [[PUB](https://openreview.net/forum?id=Jvh8HM9YEJ)] - Federated Neuro-Symbolic Learning. [[PUB](https://openreview.net/forum?id=EQXZqBXeW9)] - Adaptive Group Personalization for Federated Mutual Transfer Learning. [[PUB](https://openreview.net/forum?id=DqC9XiI71U)] - Balancing Similarity and Complementarity for Federated Learning. [[PUB](https://openreview.net/forum?id=v6tAdeCXKH)] - Federated Self-Explaining GNNs with Anti-shortcut Augmentations. [[PUB](https://openreview.net/forum?id=ZxDqSBgFSM)] - A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization. [[PUB](https://openreview.net/forum?id=NkN6wrYXe5)] - COALA: A Practical and Vision-Centric Federated Learning Platform. [[PUB](https://openreview.net/forum?id=ATRnM8PyQX)] [[CODE](https://github.com/SonyResearch/COALA)] - Collaborative Learning with Different Labeling Functions. [[PUB](https://proceedings.mlr.press/v235/deng24d.html)] - EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting. [[PUB](https://proceedings.mlr.press/v235/wang24w.html)] - Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation. [[PUB](https://proceedings.mlr.press/v235/pu24a.html)] - Localizing Task Information for Improved Model Merging and Compression. [[PUB](https://proceedings.mlr.press/v235/wang24k.html)] - Merging Multi-Task Models via Weight-Ensembling Mixture of Experts. [[PUB](https://proceedings.mlr.press/v235/tang24e.html)] - Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering. [[PUB](https://proceedings.mlr.press/v235/li24cq.html)] - Representation Surgery for Multi-Task Model Merging. [[PUB](https://proceedings.mlr.press/v235/yang24t.html)] - Socialized Learning: Making Each Other Better Through Multi-Agent Collaboration. [[PUB](https://proceedings.mlr.press/v235/yao24d.html)] - Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models. [[PUB](https://proceedings.mlr.press/v235/mergny24a.html)] #### Mach Learn - Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization. [[PUB](https://link.springer.com/article/10.1007/s10994-024-06541-y)] - Communication-efficient clustered federated learning via model distance. [[PUB](https://link.springer.com/article/10.1007/s10994-023-06443-5)] - Federated learning with superquantile aggregation for heterogeneous data. [[PUB](https://link.springer.com/article/10.1007/s10994-023-06332-x)] [[PDF](https://arxiv.org/abs/2112.09429)] [[CODE](https://github.com/krishnap25/simplicial-fl)] - Aligning model outputs for class imbalanced non-IID federated learning. [[PUB](https://link.springer.com/article/10.1007/s10994-022-06241-5)] #### TPAMI - Federated Learning of Generalized Linear Causal Networks. [[PUB](https://ieeexplore.ieee.org/document/10480288)] - Cross-Modal Federated Human Activity Recognition. [[PUB](https://ieeexplore.ieee.org/document/10440498)] - Federated Gaussian Process: Convergence, Automatic Personalization and Multi-Fidelity Modeling. [[PUB](https://ieeexplore.ieee.org/document/10402074)] [[PDF](https://arxiv.org/abs/2111.14008)] [[CODE](https://github.com/UMDataScienceLab/Federated_Gaussian_Process)] - The Impact of Adversarial Attacks on Federated Learning: A Survey. [[PUB](https://ieeexplore.ieee.org/document/10274102)] - Understanding and Mitigating Dimensional Collapse in Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10336535)] [[PDF](https://arxiv.org/abs/2210.00226)] [[CODE](https://github.com/bytedance/FedDecorr)] - No One Left Behind: Real-World Federated Class-Incremental Learning. [[PUB](https://ieeexplore.ieee.org/document/10323204)] [[PDF](https://arxiv.org/abs/2302.00903)] [[CODE](https://github.com/JiahuaDong/LGA)] - Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning. [[PUB](https://ieeexplore.ieee.org/document/10295990)] [[PDF](https://arxiv.org/abs/2309.16286)] [[CODE](https://github.com/WenkeHuang/FCCL)] - Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy. [[PUB](https://ieeexplore.ieee.org/document/10316599)] [[PDF](https://arxiv.org/abs/1912.07902)] [[CODE](https://github.com/IoTDATALab/MAPA)] - A Bayesian Federated Learning Framework With Online Laplace Approximation. [[PUB](https://ieeexplore.ieee.org/document/10274722)] [[PDF](https://arxiv.org/abs/2102.01936)] [[CODE](https://github.com/Klitter/A-Bayesian-Federated-Learning-Framework-with-Online-Laplace-Approximation)] - Federated Feature Augmentation and Alignment. [[PUB](https://doi.org/10.1109/TPAMI.2024.3457751)] - Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark. [[PUB](https://doi.org/10.1109/TPAMI.2024.3418862)] - Gradient Inversion Attacks: Impact Factors Analyses and Privacy Enhancement. [[PUB](https://doi.org/10.1109/TPAMI.2024.3430533)] - Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification. [[PUB](https://doi.org/10.1109/TPAMI.2023.3334741)] - Improved Diversity-Promoting Collaborative Metric Learning for Recommendation. [[PUB](https://doi.org/10.1109/TPAMI.2024.3412687)] #### ICLR - Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. [[PUB](https://openreview.net/forum?id=tm8s3696Ox)] - One-shot Empirical Privacy Estimation for Federated Learning. [[PUB](https://openreview.net/forum?id=0BqyZSWfzo)] [[PDF](https://arxiv.org/abs/2302.03098)] - Stochastic Controlled Averaging for Federated Learning with Communication Compression. [[PUB](https://openreview.net/forum?id=jj5ZjZsWJe)] [[PDF](https://arxiv.org/abs/2308.08165)] - A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging. [[PUB](https://openreview.net/forum?id=ZKEuFKfCKA)] [[PDF](https://arxiv.org/abs/2306.03401)] [[CODE](https://github.com/IBM/fedau)] - A Mutual Information Perspective on Federated Contrastive Learning. [[PUB](https://openreview.net/forum?id=JrmPG9ufKg)] - Benchmarking Algorithms for Federated Domain Generalization. [[PUB](https://openreview.net/forum?id=wprSv7ichW)] [[PDF](https://arxiv.org/abs/2307.04942)] [[CODE](https://github.com/inouye-lab/FedDG_Benchmark)] - Effective and Efficient Federated Tree Learning on Hybrid Data. [[PUB](https://openreview.net/forum?id=py4ZV2qYQI)] [[PDF](https://arxiv.org/abs/2310.11865)] - Federated Recommendation with Additive Personalization. [[PUB](https://openreview.net/forum?id=xkXdE81mOK)] [[PDF](https://arxiv.org/abs/2301.09109)] [[CODE](https://github.com/mtics/FedRAP)] - Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration. [[PUB](https://openreview.net/forum?id=4aywmeb97I)] [[SUPP](https://openreview.net/attachment?id=4aywmeb97I&name=supplementary_material)] - Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning. [[PUB](https://openreview.net/forum?id=nAs4LdaP9Y)] [[SUPP](https://openreview.net/attachment?id=nAs4LdaP9Y&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2309.01289)] - Accurate Forgetting for Heterogeneous Federated Continual Learning. [[PUB](https://openreview.net/forum?id=ShQrnAsbPI)] [[CODE](https://anonymous.4open.science/r/AF-FCL-7D65)] - Federated Causal Discovery from Heterogeneous Data. [[PUB](https://openreview.net/forum?id=m7tJxajC3G)] [[PDF](https://arxiv.org/abs/2402.13241)] [[CODE](https://github.com/lokali/FedCDH)] - On Differentially Private Federated Linear Contextual Bandits. [[PUB](https://openreview.net/forum?id=cuAxSHcsSX)] [[SUPP](https://openreview.net/attachment?id=cuAxSHcsSX&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2302.13945)] - Incentivized Truthful Communication for Federated Bandits. [[PUB](https://openreview.net/forum?id=ykEixGIJYb)] [[PDF](https://arxiv.org/abs/2402.04485)] - Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting. [[PUB](https://openreview.net/forum?id=6J3ehSUrMU)] - FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity. [[PUB](https://openreview.net/forum?id=hbHwZYqk9T)] - Text-driven Prompt Generation for Vision-Language Models in Federated Learning. [[PUB](https://openreview.net/forum?id=NW31gAylIm)] [[PDF](https://arxiv.org/abs/2310.06123)] - Improving LoRA in Privacy-preserving Federated Learning. [[PUB](https://openreview.net/forum?id=NLPzL6HWNl)] - FedWon: Triumphing Multi-domain Federated Learning Without Normalization. [[PUB](https://openreview.net/forum?id=hAYHmV1gM8)] [[PDF](https://arxiv.org/abs/2306.05879)] - FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning. [[PUB](https://openreview.net/forum?id=DRu8PMHgCh)] - FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler. [[PUB](https://openreview.net/forum?id=msXxrttLOi)] [[PDF](https://arxiv.org/abs/2309.14675)] [[CODE](https://github.com/APPFL/FedCompass)] [[PAGE](https://appfl.github.io/FedCompass)] - Bayesian Coreset Optimization for Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=uz7d2N2zul)] - Layer-wise linear mode connectivity. [[PUB](https://openreview.net/forum?id=LfmZh91tDI)] [[PDF](https://arxiv.org/abs/2307.06966)] [[SUPP](https://openreview.net/attachment?id=LfmZh91tDI&name=supplementary_material)] - Fake It Till Make It: Federated Learning with Consensus-Oriented Generation. [[PUB](https://openreview.net/forum?id=NY3wMJuaLf)] [[PDF](https://arxiv.org/abs/2312.05966)] - Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning. [[PUB](https://openreview.net/forum?id=krx55l2A6G)] [[SUPP](https://openreview.net/attachment?id=krx55l2A6G&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.03013)] - Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning. [[PUB](https://openreview.net/forum?id=D2eOVqPX9g)] [[PDF](https://arxiv.org/abs/2401.15273)] - Adaptive Federated Learning with Auto-Tuned Clients. [[PUB](https://openreview.net/forum?id=g0mlwqs8pi)] [[SUPP](https://openreview.net/attachment?id=g0mlwqs8pi&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.11201)] - Backdoor Federated Learning by Poisoning Backdoor-Critical Layers. [[PUB](https://openreview.net/forum?id=AJBGSVSTT2)] [[SUPP](https://openreview.net/attachment?id=AJBGSVSTT2&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2308.04466)] - Federated Q-Learning: Linear Regret Speedup with Low Communication Cost. [[PUB](https://openreview.net/forum?id=fe6ANBxcKM)] [[SUPP](https://openreview.net/attachment?id=fe6ANBxcKM&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2312.15023)] - FedImpro: Measuring and Improving Client Update in Federated Learning. [[PUB](https://openreview.net/forum?id=giU9fYGTND)] [[PDF](https://arxiv.org/abs/2402.07011)] - Federated Wasserstein Distance. [[PUB](https://openreview.net/forum?id=rsg1mvUahT)] [[SUPP](https://openreview.net/attachment?id=rsg1mvUahT&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.01973)] - An improved analysis of per-sample and per-update clipping in federated learning. [[PUB](https://openreview.net/forum?id=BdPvGRvoBC)] - FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation. [[PUB](https://openreview.net/forum?id=nbPGqeH3lt)] [[SUPP](https://openreview.net/attachment?id=nbPGqeH3lt&name=supplementary_material)] - Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning. [[PUB](https://openreview.net/forum?id=Cc0qk6r4Nd)] [[PDF](https://arxiv.org/abs/2308.11464)] - Momentum Benefits Non-iid Federated Learning Simply and Provably. [[PUB](https://openreview.net/forum?id=TdhkAcXkRi)] [[PDF](https://arxiv.org/abs/2306.16504)] - Communication-Efficient Federated Non-Linear Bandit Optimization. [[PUB](https://openreview.net/forum?id=nFI3wFM9yN)] [[PDF](https://arxiv.org/abs/2311.01695)] - Fair and Efficient Contribution Valuation for Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=sLQb8q0sUi)] [[SUPP](https://openreview.net/attachment?id=sLQb8q0sUi&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2201.02658)] [[CODE](https://github.com/zhenanf/VerFedLogistic.jl)] - Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. [[PUB](https://openreview.net/forum?id=SBj2Qdhgew)] [[PDF](https://arxiv.org/abs/2307.11333)] - Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients. [[PUB](https://openreview.net/forum?id=8FHWkY0SwF)] - PeFLL: Personalized Federated Learning by Learning to Learn. [[PUB](https://openreview.net/forum?id=MrYiwlDRQO)] [[SUPP](https://openreview.net/attachment?id=MrYiwlDRQO&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.05515)] - Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. [[PUB](https://openreview.net/forum?id=hORCalGn3Z)] [[SUPP](https://openreview.net/attachment?id=hORCalGn3Z&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.05100)] - FedInverse: Evaluating Privacy Leakage in Federated Learning. [[PUB](https://openreview.net/forum?id=nTNgkEIfeb)] [[SUPP](https://openreview.net/attachment?id=nTNgkEIfeb&name=supplementary_material)] - FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization. [[PUB](https://openreview.net/forum?id=kjn99xFUF3)] [[SUPP](https://openreview.net/attachment?id=kjn99xFUF3&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2302.06103)] - Robust Training of Federated Models with Extremely Label Deficiency. [[PUB](https://openreview.net/forum?id=qxLVaYbsSI)] [[PDF](https://arxiv.org/abs/2402.14430)] [[CODE](https://github.com/visitworld123/Twin-sight)] - Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory. [[PUB](https://openreview.net/forum?id=EcetCr4trp)] - Teach LLMs to Phish: Stealing Private Information from Language Models. [[PUB](https://openreview.net/forum?id=qo21ZlfNu6)] - Like Oil and Water: Group Robustness Methods and Poisoning Defenses Don't Mix. [[PUB](https://openreview.net/forum?id=rM9VJPB20F)] - Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise. [[PUB](https://openreview.net/forum?id=CIqjp9yTDq)] [[PDF](https://arxiv.org/abs/2312.14567)] - Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks. [[PUB](https://openreview.net/forum?id=s8cMuxI5gu)] [[SUPP](https://openreview.net/attachment?id=s8cMuxI5gu&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2402.03124)] [[CODE](https://github.com/ybwang119/label_recovery)] - Local Composite Saddle Point Optimization. [[PUB](https://openreview.net/forum?id=kklwv4c4dI)] [[PDF](https://arxiv.org/abs/2305.15643)] - Enhancing Neural Training via a Correlated Dynamics Model. [[PUB](https://openreview.net/forum?id=c9xsaASm9L)] [[PDF](https://arxiv.org/abs/2312.13247)] - EControl: Fast Distributed Optimization with Compression and Error Control. [[PUB](https://openreview.net/forum?id=lsvlvWB9vz)] [[SUPP](https://openreview.net/attachment?id=lsvlvWB9vz&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2311.05645)] - Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit. [[PUB](https://openreview.net/forum?id=m52uU0dVbH)] - FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent. [[PUB](https://openreview.net/forum?id=Kl9CqKf7h6)] [[SUPP](https://openreview.net/attachment?id=Kl9CqKf7h6&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.03156)] [[CODE](https://github.com/ATP-1010/FedHyper)] - Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate. [[PUB](https://openreview.net/forum?id=7pWRLDBAtc)] - Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. [[PUB](https://openreview.net/forum?id=zzqn5G9fjn)] - Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity. [[PUB](https://openreview.net/forum?id=1ii8idH4tH)] - VFLAIR: A Research Library and Benchmark for Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=sqRgz88TM3)] [[PDF](https://arxiv.org/abs/2310.09827)] [[CODE](https://github.com/FLAIR-THU/VFLAIR)] - Incentive-Aware Federated Learning with Training-Time Model Rewards. [[PUB](https://openreview.net/forum?id=FlY7WQ2hWS)] [[SUPP](https://openreview.net/attachment?id=FlY7WQ2hWS&name=supplementary_material)] - VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks. [[PUB](https://openreview.net/forum?id=glwwbaeKm2)] [[PDF](https://arxiv.org/abs/2307.02040)] [[CODE](https://github.com/Xtra-Computing/VertiBench)] - FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data. [[PUB](https://openreview.net/forum?id=V3j5d0GQgH)] [[SUPP](https://openreview.net/attachment?id=V3j5d0GQgH&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2401.08977)] - Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. [[PUB](https://openreview.net/forum?id=SBj2Qdhgew)] - Federated Text-driven Prompt Generation for Vision-Language Models. [[PUB](https://openreview.net/forum?id=NW31gAylIm)] - A Good Learner can Teach Better: Teacher-Student Collaborative Knowledge Distillation. [[PUB](https://openreview.net/forum?id=Ixi4j6LtdX)] - AdaMerging: Adaptive Model Merging for Multi-Task Learning. [[PUB](https://openreview.net/forum?id=nZP6NgD3QY)] - CLAP: Collaborative Adaptation for Patchwork Learning. [[PUB](https://openreview.net/forum?id=8EyRkd3Qj2)] - CO2: Efficient Distributed Training with Full Communication-Computation Overlap. [[PUB](https://openreview.net/forum?id=ZO5cn4IfaN)] - Model Merging by Uncertainty-Based Gradient Matching. [[PUB](https://openreview.net/forum?id=D7KJmfEDQP)] - ZipIt! Merging Models from Different Tasks without Training. [[PUB](https://openreview.net/forum?id=LEYUkvdUhq)] ### 2023 #### machine learning - Ensemble and continual federated learning for classification tasks. [[PUB](https://doi.org/10.1007/s10994-023-06330-z)] - FAC-fed: Federated adaptation for fairness and concept drift aware stream classification. [[PUB](https://doi.org/10.1007/s10994-023-06360-7)] - Robust federated learning under statistical heterogeneity via hessian-weighted aggregation. [[PUB](https://doi.org/10.1007/s10994-022-06292-8)] #### NeurIPS - SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning. [[PUB](https://openreview.net/forum?id=ZdxGmJGKOo)] [[PDF](https://arxiv.org/abs/2305.19442)] [[SUPP](https://openreview.net/attachment?id=ZdxGmJGKOo&name=supplementary_material)] - Mechanism Design for Collaborative Normal Mean Estimation. [[PUB](https://openreview.net/forum?id=yKCLfOOIL7)] [[PDF](https://arxiv.org/abs/2306.06351)] - Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. [[PUB](https://openreview.net/forum?id=n3fPDW87is)] [[PDF](https://arxiv.org/abs/2309.13591)] [[CODE](https://github.com/GeovaniRizk/Robust-Distributed-Learning-Tight-Error-Bounds-and-Breakdown-Point-under-Data-Heterogeneity)] - Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization. [[PUB](https://openreview.net/forum?id=9OqezkNxnX)] [[SUPP](https://openreview.net/attachment?id=9OqezkNxnX&name=supplementary_material)] - Convergence Analysis of Sequential Federated Learning on Heterogeneous Data. [[PUB](https://openreview.net/forum?id=Dxhv8Oja2V)] [[PDF](https://arxiv.org/abs/2311.03154)] [[CODE](https://github.com/liyipeng00/convergence)] - Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition. [[PUB](https://openreview.net/forum?id=LGKxz9clGG)] [[PDF](https://arxiv.org/abs/2310.15165)] [[CODE](https://github.com/sarapieri/fed_het.git)] - Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. [[PUB](https://openreview.net/forum?id=rzDBoh1tBh)] [[SUPP](https://openreview.net/attachment?id=rzDBoh1tBh&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.09396)] - Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization. [[PUB](https://openreview.net/forum?id=46x3zvYCyQ)] [[SUPP](https://openreview.net/attachment?id=46x3zvYCyQ&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2309.13024)] - Incentivized Communication for Federated Bandits. [[PUB](https://openreview.net/forum?id=1aQivXgZKj)] [[PDF](https://arxiv.org/abs/2309.11702)] - Multiply Robust Federated Estimation of Targeted Average Treatment Effects. [[PUB](https://openreview.net/forum?id=M6UccKMFGl)] [[PDF](https://arxiv.org/abs/2309.12600)] - IBA: Towards Irreversible Backdoor Attacks in Federated Learning. [[PUB](https://openreview.net/forum?id=cemEOP8YoC)] [[SUPP](https://openreview.net/attachment?id=cemEOP8YoC&name=supplementary_material)] [[CODE](https://github.com/sail-research/iba)] - EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=P3Z59Okb5I)] [[SUPP](https://openreview.net/attachment?id=P3Z59Okb5I&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2311.07485)] - Federated Linear Bandits with Finite Adversarial Actions. [[PUB](https://openreview.net/forum?id=bzXpQUnule)] [[SUPP](https://openreview.net/attachment?id=bzXpQUnule&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2311.00973)] - FedNAR: Federated Optimization with Normalized Annealing Regularization. [[PUB](https://openreview.net/forum?id=x5fs7TXKDc)] [[SUPP](https://openreview.net/attachment?id=x5fs7TXKDc&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.03163)] [[CODE](https://github.com/ljb121002/fednar)] - Guiding The Last Layer in Federated Learning with Pre-Trained Models. [[PUB](https://openreview.net/forum?id=HRGd5dcVfw)] [[SUPP](https://openreview.net/attachment?id=HRGd5dcVfw&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2306.03937)] [[CODE](https://github.com/GwenLegate/GuidingLastLayerFLPretrain)] - Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization. [[PUB](https://openreview.net/forum?id=0ycX03sMAT)] [[SUPP](https://openreview.net/attachment?id=0ycX03sMAT&name=supplementary_material)] - Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection. [[PUB](https://openreview.net/forum?id=2D7ou48q0E)] [[SUPP](https://openreview.net/attachment?id=2D7ou48q0E&name=supplementary_material)] [[PDF](https://arxiv.org/abs/2310.17097)] [[CODE](https://github.com/Kthyeon/ssfod)] - A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks. [[PUB](https://openreview.net/forum?id=3b9sqxCW1x)] [[PDF](https://arxiv.org/abs/2311.07784)] [[CODE](https://github.com/SaraBabakN/MFCL-NeurIPS23)] - Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning. [[PUB](https://openreview.net/forum?id=qJJmu4qsLO)] [[SUPP](https://openreview.net/attachment?id=qJJmu4qsLO&name=supplementary_material)] - One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. [[PUB](https://openreview.net/forum?id=KMxRQO7P98)] [[SUPP](https://openreview.net/attachment?id=KMxRQO7P98&name=supplementary_material)] [[CODE](https://github.com/lzcemma/RACE_Distance)] - Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training. [[PUB](https://openreview.net/forum?id=V5cQH7JbGo)] [[SUPP](https://openreview.net/attachment?id=V5cQH7JbGo&name=supplementary_material)] [[CODE](https://github.com/git-disl/Lockdown)] - FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning. [[PUB](https://openreview.net/forum?id=nX0zYBGEka)] [[SUPP](https://openreview.net/attachment?id=nX0zYBGEka&name=supplementary_material)] [[CODE](https://github.com/AI-secure/FedGame)] - Towards Personalized Federated Learning via Heterogeneous Model Reassembly. 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[[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/431d53d513461ff155d5bc8faa9a440c-Abstract-Conference.html)] - Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/662bb9c4dcc96aeaac8e7cd3fc6a0add-Abstract-Datasets_and_Benchmarks.html)] [[CODE](https://github.com/google-research/dataset_grouper)] - Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/02b9d1e6d1b5295a6f883969ddc1bbbd-Abstract-Datasets_and_Benchmarks.html)] - Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/7c72fcd7b6bffc3864c7152ab5a2dd83-Abstract-Conference.html)] - Collaborative Learning via Prediction Consensus. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/065e259a1d2d955e63b99aac6a3a3081-Abstract-Conference.html)] - Incentives in Private Collaborative Machine Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/180f1a1de4244c009ff0848c55ae54a5-Abstract-Conference.html)] - Robust and Actively Secure Serverless Collaborative Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/7c5a4b7a31dffef8ce296deedb6214a9-Abstract-Conference.html)] - Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/5b4a459db23e6db9be2a128380953d96-Abstract-Conference.html)] - Swarm Reinforcement Learning for Adaptive Mesh Refinement. [[PUB](http://papers.nips.cc/paper_files/paper/2023/hash/e85454a113e8b41e017c81875ae68d47-Abstract-Conference.html)] #### NeurIPS Datasets and Benchmarks - Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. [[PUB](https://openreview.net/forum?id=qynH28Y4xE)] [[SUPP](https://openreview.net/attachment?id=qynH28Y4xE&name=supplementary_material)] [[DATASET](https://huggingface.co/datasets/wyzelabs/RuleRecommendation)] - Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. [[PUB](https://openreview.net/forum?id=EPz1DcdPVE)] [[PDF](https://arxiv.org/abs/2307.09619)] [[DATASET](https://github.com/google-research/dataset_grouper)] [[CODE](https://github.com/google-research/dataset_grouper)] #### NeurIPS workshop - Text-driven Prompt Generation for Vision-Language Models in Federated Learning. [[PUB](https://openreview.net/forum?id=8zduZGpzZl)] - HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning. [[PUB](https://openreview.net/forum?id=dsWg7n6zoo)] - Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning. [[PUB](https://openreview.net/forum?id=H0inHCV05c)] - FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data. [[PUB](https://openreview.net/forum?id=XJhL1XlefX)] - FedSoL: Bridging Global Alignment and Local Generality in Federated Learning. [[PUB](https://openreview.net/forum?id=WYLhRgBAFH)] - One-shot Empirical Privacy Estimation for Federated Learning. [[PUB](https://openreview.net/forum?id=JmrHzzDiyI)] - Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning. [[PUB](https://openreview.net/forum?id=5JsO2DClwk)] - SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models. [[PUB](https://openreview.net/forum?id=06quMTmtRV)] - The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning. [[PUB](https://openreview.net/forum?id=xqvB784PCv)] - Towards Building the FederatedGPT: Federated Instruction Tuning. [[PUB](https://openreview.net/forum?id=TaDiklyVps)] - Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR. [[PUB](https://openreview.net/forum?id=ozN92d7CHX)] - LASER: Linear Compression in Wireless Distributed Optimization. [[PUB](https://openreview.net/forum?id=PmahoyE89G)] - MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization. [[PUB](https://openreview.net/forum?id=YqqWQP8POe)] - TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation. [[PUB](https://openreview.net/forum?id=SvJx4a75QZ)] - An Empirical Evaluation of Federated Contextual Bandit Algorithms. [[PUB](https://openreview.net/forum?id=qwnOt7FFSD)] - RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation. [[PUB](https://openreview.net/forum?id=FakNykU4PF)] - FDAPT: Federated Domain-adaptive Pre-training for Language Models. [[PUB](https://openreview.net/forum?id=ESCL5T3EgV)] - Making Batch Normalization Great in Federated Deep Learning. [[PUB](https://openreview.net/forum?id=iKQC652XIk)] - Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. [[PUB](https://openreview.net/forum?id=AbrnDOw8R9)] - Parameter Averaging Laws for Multitask Language Models. [[PUB](https://openreview.net/forum?id=qQ2qXFu05s)] - Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. [[PUB](https://openreview.net/forum?id=HyRwexERAo)] - Beyond Parameter Averaging in Model Aggregation. [[PUB](https://openreview.net/forum?id=sPtEDSVD4K)] - Augmenting Federated Learning with Pretrained Transformers. [[PUB](https://openreview.net/forum?id=ldN6QdyukS)] - Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization. [[PUB](https://openreview.net/forum?id=le0Emy9SqA)] - DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization. [[PUB](https://openreview.net/forum?id=s7hquGszME)] - Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning. [[PUB](https://openreview.net/forum?id=gACRiXPGmM)] - FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System. [[PUB](https://openreview.net/forum?id=PuYD0fh5aq)] - Learning Optimizers for Local SGD. [[PUB](https://openreview.net/forum?id=HiPe4SjZMs)] - Exploring User-level Gradient Inversion with a Diffusion Prior. [[PUB](https://openreview.net/forum?id=lcElZPvMFp)] - User Inference Attacks on Large Language Models. [[PUB](https://openreview.net/forum?id=4uyyLG4KCH)] - FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis. [[PUB](https://openreview.net/forum?id=1ww9tjEQVL)] - Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models. [[PUB](https://openreview.net/forum?id=EmV9sGpZ7q)] - Backdoor Threats from Compromised Foundation Models to Federated Learning. [[PUB](https://openreview.net/forum?id=BrcHuO2BVc)] - MOFL/D: A Federated Multi-objective Learning Framework with Decomposition. [[PUB](https://openreview.net/forum?id=Pj6BPHZy56)] - Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning. [[PUB](https://openreview.net/forum?id=OoEIUohfcp)] - Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models. [[PUB](https://openreview.net/forum?id=LiSj1GRVhL)] - FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning. [[PUB](https://openreview.net/forum?id=4apX9Kcxie)] - Private and Personalized Histogram Estimation in a Federated Setting. [[PUB](https://openreview.net/forum?id=XSfsvBoc8M)] #### COLT - The Aggregation–Heterogeneity Trade-off in Federated Learning. [[PUB](https://proceedings.mlr.press/v195/zhao23b.html)] #### UAI - FLASH: Automating federated learning using CASH. [[PUB](https://openreview.net/forum?id=5L66DZpPSHk)] [[SUPP](https://proceedings.mlr.press/v216/alam23a/alam23a-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=5L66DZpPSHk&name=other_supplementary_material)] - Personalized federated domain adaptation for item-to-item recommendation. [[PUB](https://openreview.net/forum?id=7ypu4_en3Zm)] [[PDF](https://arxiv.org/abs/2306.03191)] [[SUPP](https://proceedings.mlr.press/v216/fan23a/fan23a-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=7ypu4_en3Zm&name=other_supplementary_material)] [[CODE](https://github.com/zfan20/PFGNNPlus)] - Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning. [[PUB](https://openreview.net/forum?id=Q06wKxnHRv)] [[PDF](https://arxiv.org/abs/2110.00532)] [[SUPP](https://proceedings.mlr.press/v216/karimi23a/karimi23a-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=Q06wKxnHRv&name=other_supplementary_material)] - Federated learning of models pre-trained on different features with consensus graphs. [[PUB](https://openreview.net/forum?id=gSMiXJmMEOf)] [[SUPP](https://proceedings.mlr.press/v216/ma23b/ma23b-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=gSMiXJmMEOf&name=other_supplementary_material)] [[CODE](https://github.com/matenure/federated_feature_fusion)] - Fast Heterogeneous Federated Learning with Hybrid Client Selection. [[PUB](https://openreview.net/forum?id=JtSlA972EHP)] [[SUPP](https://proceedings.mlr.press/v216/song23b/song23b-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=JtSlA972EHP&name=other_supplementary_material)] [[PDF](https://arxiv.org/abs/2208.05135)] - Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning. [[PUB](https://openreview.net/forum?id=Gt_GiNkBhu)] [[PDF](https://arxiv.org/abs/2210.10880)] [[SUPP](https://proceedings.mlr.press/v216/wu23a/wu23a-supp.pdf)] [[MATERIAL](https://openreview.net/attachment?id=Gt_GiNkBhu&name=other_supplementary_material)] [[CODE](https://github.com/wrh14/learning_to_invert)] #### ICML - Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. [[PUB](https://openreview.net/forum?id=vD1R00hROK)] [[PDF](https://arxiv.org/abs/2305.11584)] [[SLIDES](https://icml.cc/media/icml-2023/Slides/24651.pdf)] - Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation. [[PUB](https://openreview.net/forum?id=wbs1fKLfOe)] [[PDF](https://arxiv.org/abs/2211.14292)] - FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization. [[PUB](https://openreview.net/forum?id=891ytYlYgB)] [[PDF](https://arxiv.org/abs/2206.03966)] [[CODE](https://github.com/alibaba/FederatedScope/tree/master/benchmark/FedHPOBench)] - Federated Conformal Predictors for Distributed Uncertainty Quantification. [[PUB](https://openreview.net/forum?id=YVTr9PzIrK)] [[PDF](https://arxiv.org/abs/2305.17564)] [[CODE](https://github.com/clu5/federated-conformal)] - Federated Adversarial Learning: A Framework with Convergence Analysis. [[PUB](https://openreview.net/forum?id=kgvoV2KcTJ)] [[PDF](https://arxiv.org/abs/2208.03635)] - Federated Heavy Hitter Recovery under Linear Sketching. [[PUB](https://openreview.net/forum?id=zN4oRCrlnM)] [[PDF](https://arxiv.org/abs/2307.13347)] [[CODE](https://github.com/google-research/federated)] - Doubly Adversarial Federated Bandits. [[PUB](https://openreview.net/forum?id=FjOB0g7iRf)] [[PDF](https://arxiv.org/abs/2301.09223)] [[CODE](https://github.com/jialinyi94/doubly-stochastic-federataed-bandit)] - Achieving Linear Speedup in Non-IID Federated Bilevel Learning. [[PUB](https://openreview.net/forum?id=XFpTtAWNpQ)] [[PDF](https://arxiv.org/abs/2302.05412)] - One-Shot Federated Conformal Prediction. [[PUB](https://openreview.net/forum?id=SZJGIWe1Ag)] [[PDF](https://arxiv.org/abs/2302.06322)] [[CODE](https://github.com/pierreHmbt/FedCP-QQ)] - Federated Online and Bandit Convex Optimization. [[PUB](https://openreview.net/forum?id=mi7pnouqLa)] - Federated Linear Contextual Bandits with User-level Differential Privacy. [[PUB](https://openreview.net/forum?id=b9opfVNw6O)] [[PDF](https://arxiv.org/abs/2306.05275)] - Vertical Federated Graph Neural Network for Recommender System. [[PUB](https://openreview.net/forum?id=NRnS6CtbaN)] [[PDF](https://arxiv.org/abs/2303.05786)] [[CODE](https://github.com/maiph123/verticalgnn)] - Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation. [[PUB](https://openreview.net/forum?id=IYyhNudD9V)] [[PDF](https://arxiv.org/abs/2302.04969)] - Towards Understanding Ensemble Distillation in Federated Learning. [[PUB](https://openreview.net/forum?id=Xx0TH4IKgQ)] - Personalized Subgraph Federated Learning. [[PUB](https://openreview.net/forum?id=GXHL8ZS1GX)] [[PDF](https://arxiv.org/abs/2206.10206)] [[CODE](https://github.com/Kang-Min-Ku/CUFL.git)] - Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. [[PUB](https://openreview.net/forum?id=ytpEqHYSEy)] [[PDF](https://arxiv.org/abs/2306.05131)] - Secure Federated Correlation Test and Entropy Estimation. [[PUB](https://openreview.net/forum?id=ICk7GJ1awE)] [[PDF](https://arxiv.org/abs/2105.14618)] - Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships. [[PUB](https://openreview.net/forum?id=JC05k0E2EM)] [[CODE](https://github.com/YamingGuo98/FedIIR)] - Personalized Federated Learning under Mixture of Distributions. [[PUB](https://openreview.net/forum?id=nmVOTsQGR9)] [[PDF](https://arxiv.org/abs/2305.01068)] [[CODE](https://github.com/zshuai8/FedGMM_ICML2023)] - FedDisco: Federated Learning with Discrepancy-Aware Collaboration. [[PUB](https://openreview.net/forum?id=cHJ1VuZorx)] [[PDF](https://arxiv.org/abs/2305.19229)] [[CODE](https://github.com/MediaBrain-SJTU/FedDisco)] - Anchor Sampling for Federated Learning with Partial Client Participation. [[PUB](https://openreview.net/forum?id=Ht9r3P6Lts)] [[PDF](https://arxiv.org/abs/2206.05891)] [[CODE](https://github.com/harliwu/fedamd)] - Private Federated Learning with Autotuned Compression. [[PUB](https://openreview.net/forum?id=y8qAZhWbNs)] [[PDF](https://arxiv.org/abs/2307.10999)] - Fast Federated Machine Unlearning with Nonlinear Functional Theory. [[PUB](https://openreview.net/forum?id=6wQKmKiDHw)] - On the Convergence of Federated Averaging with Cyclic Client Participation. [[PUB](https://openreview.net/forum?id=d8LTNXt97w)] [[PDF](https://arxiv.org/abs/2302.03109)] - Revisiting Weighted Aggregation in Federated Learning with Neural Networks. [[PUB](https://openreview.net/forum?id=FuDAjnWhrQ)] [[PDF](https://arxiv.org/abs/2302.10911)] [[CODE](https://github.com/zexilee/icml-2023-fedlaw)] - The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond. [[PUB](https://openreview.net/forum?id=WfI3I8OjHS)] [[PDF](https://arxiv.org/abs/2305.10697)] [[SLIDES](https://icml.cc/media/icml-2023/Slides/24679_ljO6pDE.pdf)] - GuardHFL: Privacy Guardian for Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=iASUTBGw07)] - Flash: Concept Drift Adaptation in Federated Learning. [[PUB](https://openreview.net/forum?id=q5RHsg6VRw)] - DoCoFL: Downlink Compression for Cross-Device Federated Learning. [[PUB](https://openreview.net/forum?id=VxKr51JjWC)] [[PDF](https://arxiv.org/abs/2302.00543)] - FeDXL: Provable Federated Learning for Deep X-Risk Optimization. [[PUB](https://openreview.net/forum?id=C7fNCYdptO)] [[PDF](https://arxiv.org/abs/2210.14396)] [[CODE](https://github.com/optimization-ai/icml2023_fedxl)] - No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation. [[PUB](https://openreview.net/forum?id=AMuNQEUmGr)] [[CODE](https://github.com/Hypervoyager/PFL)] - Personalized Federated Learning with Inferred Collaboration Graphs. [[PUB](https://openreview.net/forum?id=33fj5Ph3ot)] [[CODE](https://github.com/MediaBrain-SJTU/pFedGraph)] - Optimizing the Collaboration Structure in Cross-Silo Federated Learning. [[PUB](https://openreview.net/forum?id=rnNBSMOWvA)] [[PDF](https://arxiv.org/abs/2306.06508)] [[CODE](https://github.com/baowenxuan/fedcollab)] [[SLIDES](https://icml.cc/media/icml-2023/Slides/23569.pdf)] - TabLeak: Tabular Data Leakage in Federated Learning. [[PUB](https://openreview.net/forum?id=mRiDy4qGwB)] [[PDF](https://arxiv.org/abs/2210.01785)] [[CODE](https://github.com/eth-sri/tableak)] - FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization. [[PUB](https://openreview.net/forum?id=YDC5jTS3LR)] [[CODE](https://github.com/haozzh/FedCR)] - Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction. [[PUB](https://openreview.net/forum?id=NcbY2UOfko)] [[PDF](https://arxiv.org/abs/2209.15245)] - Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. [[PUB](https://openreview.net/forum?id=Otdp5SGQMr)] [[PDF](https://arxiv.org/abs/2211.03942)] [[CODE](https://github.com/facebookresearch/dp_compression)] - SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning. [[PUB](https://openreview.net/forum?id=pRsJIVcjxD)] [[PDF](https://arxiv.org/abs/2306.07644)] [[CODE](https://github.com/owkin/sratta)] - Improving the Model Consistency of Decentralized Federated Learning. [[PUB](https://openreview.net/forum?id=fn2NFlYLBL)] [[PDF](https://arxiv.org/abs/2302.04083)] - Efficient Personalized Federated Learning via Sparse Model-Adaptation. [[PUB](https://openreview.net/forum?id=ieSN7Xyo8g)] [[PDF](https://arxiv.org/abs/2305.02776)] [[CODE](https://github.com/yxdyc/pfedgate)] - From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. [[PUB](https://openreview.net/forum?id=CBLDv6SFMn)] [[PDF](https://arxiv.org/abs/2302.12559)] [[CODE](https://github.com/totilas/padadmm)] - LeadFL: Client Self-Defense against Model Poisoning in Federated Learning. [[PUB](https://openreview.net/forum?id=2CiaH2Tq4G)] [[CODE](https://github.com/chaoyitud/LeadFL)] - Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning. [[PUB](https://openreview.net/forum?id=HtHFnHrZXu)] [[PDF](https://arxiv.org/abs/2304.12961)] [[CODE](https://github.com/ybdai7/chameleon-durable-backdoor)] - FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models. [[PUB](https://openreview.net/forum?id=7aqVcrXjxa)] [[PDF](https://arxiv.org/abs/2304.13407)] - FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction. [[PUB](https://openreview.net/forum?id=nDKoVwNjMH)] [[PDF](https://arxiv.org/abs/2205.13462)] [[CODE](https://github.com/lins-lab/fedbr)] - Towards Unbiased Training in Federated Open-world Semi-supervised Learning. [[PUB](https://openreview.net/forum?id=gHfybro5Sj)] [[PDF](https://arxiv.org/abs/2305.00771)] [[SLIDES](https://icml.cc/media/icml-2023/Slides/25109.pdf)] - Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. [[PUB](https://openreview.net/forum?id=Ai1TyAjZt9)] [[PDF](https://arxiv.org/abs/2209.05578)] - Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. [[PUB](https://openreview.net/forum?id=Kz0IODB2kj)] [[PDF](https://arxiv.org/abs/2306.00127)] [[CODE](https://github.com/junyizhu-ai/surrogate_model_extension)] - Fair yet Asymptotically Equal Collaborative Learning. [[PUB](https://openreview.net/forum?id=5VhltFPSO8)] [[PDF](https://arxiv.org/abs/2306.05764)] [[CODE](https://github.com/xqlin98/Fair-yet-Equal-CML)] - Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability. [[PUB](https://openreview.net/forum?id=uIzkbJgyqc)] [[PDF](https://arxiv.org/abs/2210.08371)] - Adversarial Collaborative Learning on Non-IID Features. [[PUB](https://openreview.net/forum?id=DVF7gEQQf7)] - XTab: Cross-table Pretraining for Tabular Transformers. [[PUB](https://openreview.net/forum?id=uGORNDmIdr)] [[PDF](https://arxiv.org/abs/2305.06090)] [[CODE](https://github.com/bingzhaozhu/xtab)] - Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions. [[PUB](https://openreview.net/forum?id=a0kGwNUwil)] - Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting. [[PUB](https://openreview.net/forum?id=3DI6Kmw81p)] [[PDF](https://arxiv.org/abs/2302.06079)] [[CODE](https://github.com/YuchenLiu-a/byzantine-gas)] - LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=L8iWCxzwl1)] [[PDF](https://arxiv.org/abs/2305.02219)] - FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks. [[PUB](https://openreview.net/forum?id=eqTWOzheZT)] - Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. [[PUB](https://openreview.net/forum?id=iAgQfF3atY)] [[PDF](https://arxiv.org/abs/2306.02543)] [[CODE](https://github.com/boxinz17/data-market-via-adaptive-sampling)] - Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. [[PUB](https://proceedings.mlr.press/v202/kandpal23b.html)] - RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution. [[PUB](https://proceedings.mlr.press/v202/li23i.html)] - Robust Collaborative Learning with Linear Gradient Overhead. [[PUB](https://proceedings.mlr.press/v202/farhadkhani23a.html)] [[CODE](https://github.com/LPD-EPFL/robust-collaborative-learning)] #### Mach Learn - Ensemble and continual federated learning for classification tasks. [[PUB](https://link.springer.com/article/10.1007/s10994-023-06330-z)] [[PDF](https://arxiv.org/abs/2006.07129)] - FAC-fed: Federated adaptation for fairness and concept drift aware stream classification. [[PUB](https://link.springer.com/article/10.1007/s10994-023-06360-7)] - Robust federated learning under statistical heterogeneity via hessian-weighted aggregation. [[PUB](https://link.springer.com/article/10.1007/s10994-022-06292-8)] #### JMLR - FedLab: A Flexible Federated Learning Framework :fire:. [[PUB](https://jmlr.org/papers/v24/22-0440.html)] [[PDF](https://arxiv.org/abs/2107.11621)] [[CODE](https://github.com/SMILELab-FL/FedLab)] - Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?. [[PUB](https://jmlr.org/papers/v24/21-0224.html)] - Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. [[PUB](https://jmlr.org/papers/v24/21-1301.html)] [[PDF](https://arxiv.org/abs/2106.04911)] [[CODE](https://github.com/bokun-wang/moml)] - A First Look into the Carbon Footprint of Federated Learning. [[PUB](https://jmlr.org/papers/v24/21-0445.html)] [[PDF](https://arxiv.org/abs/2102.07627)] - Attacks against Federated Learning Defense Systems and their Mitigation. [[PUB](https://jmlr.org/papers/v24/22-0014.html)] [[CODE](https://github.com/codymlewis/viceroy)] - A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. [[PUB](https://jmlr.org/papers/v24/22-0689.html)] [[PDF](https://arxiv.org/abs/2206.10189)] [[CODE](https://github.com/Accenture/Labs-Federated-Learning/tree/asynchronous_FL)] - FedLab: A Flexible Federated Learning Framework. [[PUB](http://jmlr.org/papers/v24/22-0440.html)] - A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points. [[PUB](https://jmlr.org/papers/v24/22-0153.html)] - Multi-view Collaborative Gaussian Process Dynamical Systems. [[PUB](http://jmlr.org/papers/v24/19-094.html)] - Variational Inference for Deblending Crowded Starfields. [[PUB](https://jmlr.org/papers/v24/21-0169.html)] #### TPAMI - Tighter Regret Analysis and Optimization of Online Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10255290)] [[PDF](https://arxiv.org/abs/2205.06491)] - Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup. [[PDF](https://arxiv.org/abs/2308.00522)] - Federated Learning Via Inexact ADMM. [[PUB](https://ieeexplore.ieee.org/document/10040221)] [[PDF](https://arxiv.org/abs/2204.10607)] [[CODE](https://github.com/ShenglongZhou/FedADMM)] - FedIPR: Ownership Verification for Federated Deep Neural Network Models. [[PUB](https://ieeexplore.ieee.org/document/9847383)] [[PDF](https://arxiv.org/abs/2109.13236)] [[CODE](https://github.com/purp1eHaze/FedIPR)] [[解读](https://zhuanlan.zhihu.com/p/562837170)] - Decentralized Federated Averaging. [[PUB](https://ieeexplore.ieee.org/document/9850408)] [[PDF](https://arxiv.org/abs/2104.11375)] - Attribute-Guided Collaborative Learning for Partial Person Re-Identification. [[PUB](https://doi.org/10.1109/TPAMI.2023.3312302)] - Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling. [[PUB](https://doi.org/10.1109/TPAMI.2022.3141095)] #### ICLR - Personalized Federated Learning with Feature Alignment and Classifier Collaboration. [[PUB](https://openreview.net/forum?id=SXZr8aDKia)] [[CODE](https://github.com/JianXu95/FedPAC)] - MocoSFL: enabling cross-client collaborative self-supervised learning. [[PUB](https://openreview.net/forum?id=2QGJXyMNoPz)] [[CODE](https://github.com/SonyAI/MocoSFL)] - Single-shot General Hyper-parameter Optimization for Federated Learning. [[PUB](https://openreview.net/forum?id=3RhuF8foyPW)] [[PDF](https://arxiv.org/abs/2202.08338)] [[CODE](https://openreview.net/attachment?id=3RhuF8foyPW&name=SUPP_material)] - Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated. [[PUB](https://openreview.net/forum?id=Mpa3tRJFBb)] [[PDF](https://arxiv.org/abs/2206.15387)] [[CODE](https://github.com/facebookresearch/where_to_begin)] - FedExP: Speeding up Federated Averaging via Extrapolation. [[PUB](https://openreview.net/forum?id=IPrzNbddXV)] [[PDF](https://arxiv.org/abs/2301.09604)] [[CODE](https://github.com/divyansh03/fedexp)] - Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection. [[PUB](https://openreview.net/forum?id=mMNimwRb7Gr)] [[CODE](https://github.com/illidanlab/FOSTER)] - DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. [[PUB](https://openreview.net/forum?id=VA1YpcNr7ul)] [[PDF](https://arxiv.org/abs/2202.01268)] [[CODE](https://github.com/mysteryresearcher/dasha)] - Machine Unlearning of Federated Clusters. [[PUB](https://openreview.net/forum?id=VzwfoFyYDga)] [[PDF](https://arxiv.org/abs/2210.16424)] [[CODE](https://openreview.net/attachment?id=VzwfoFyYDga&name=SUPP_material)] - Federated Neural Bandits. [[PUB](https://openreview.net/forum?id=38m4h8HcNRL)] [[PDF](https://arxiv.org/abs/2205.14309)] [[CODE](https://openreview.net/attachment?id=38m4h8HcNRL&name=SUPP_material)] - FedFA: Federated Feature Augmentation. [[PUB](https://openreview.net/forum?id=U9yFP90jU0)] [[PDF](https://arxiv.org/abs/2301.12995)] [[CODE](https://github.com/tfzhou/fedfa)] - Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. [[PUB](https://openreview.net/forum?id=dZrQR7OR11)] [[PDF](https://arxiv.org/abs/2302.04228)] [[CODE](https://github.com/hanguo97/expectation-propagation)] - Better Generative Replay for Continual Federated Learning. [[PUB](https://openreview.net/forum?id=cRxYWKiTan)] [[CODE](https://github.com/daiqing98/FedCIL)] - Federated Learning from Small Datasets. [[PUB](https://openreview.net/forum?id=hDDV1lsRV8)] [[PDF](https://arxiv.org/abs/2110.03469)] - Federated Nearest Neighbor Machine Translation. [[PUB](https://openreview.net/forum?id=R1U5G2spbLd)] [[PDF](https://arxiv.org/abs/2302.12211)] - Meta Knowledge Condensation for Federated Learning. [[PUB](https://openreview.net/forum?id=TDf-XFAwc79)] [[PDF](https://arxiv.org/abs/2209.14851)] - Test-Time Robust Personalization for Federated Learning. [[PUB](https://openreview.net/forum?id=3aBuJEza5sq)] [[PDF](https://arxiv.org/abs/2205.10920)] [[CODE](https://openreview.net/attachment?id=3aBuJEza5sq&name=SUPP_material)] - DepthFL : Depthwise Federated Learning for Heterogeneous Clients. [[PUB](https://openreview.net/forum?id=pf8RIZTMU58)] - Towards Addressing Label Skews in One-Shot Federated Learning. [[PUB](https://openreview.net/forum?id=rzrqh85f4Sc)] [[CODE](https://openreview.net/attachment?id=rzrqh85f4Sc&name=SUPP_material)] - Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning. [[PUB](https://openreview.net/forum?id=EXnIyMVTL8s)] [[PDF](https://arxiv.org/abs/2210.00226)] [[CODE](https://github.com/Yujun-Shi/FedCLS)] - Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. [[PUB](https://openreview.net/forum?id=A9WQaxYsfx)] [[CODE](https://openreview.net/attachment?id=A9WQaxYsfx&name=SUPP_material)] - SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication. [[PUB](https://openreview.net/forum?id=jh1nCir1R3d)] [[PDF](https://arxiv.org/abs/2210.14026)] [[CODE](https://openreview.net/attachment?id=jh1nCir1R3d&name=SUPP_material)] - Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. [[PUB](https://openreview.net/forum?id=TVY6GoURrw)] [[PDF](https://arxiv.org/abs/2106.09779)] [[CODE](https://github.com/lowya/private-federated-learning-without-a-trusted-server)] - Effective passive membership inference attacks in federated learning against overparameterized models. [[PUB](https://openreview.net/forum?id=QsCSLPP55Ku)] - FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. [[PUB](https://openreview.net/forum?id=9aokcgBVIj1)] [[PDF](https://arxiv.org/abs/2206.08671)] [[CODE](https://openreview.net/attachment?id=9aokcgBVIj1&name=SUPP_material)] - Multimodal Federated Learning via Contrastive Representation Ensemble. [[PUB](https://openreview.net/forum?id=Hnk1WRMAYqg)] [[PDF](https://arxiv.org/abs/2302.08888)] [[CODE](https://github.com/flair-thu/creamfl)] - Faster federated optimization under second-order similarity. [[PUB](https://openreview.net/forum?id=ElC6LYO4MfD)] [[PDF](https://arxiv.org/abs/2209.02257)] [[CODE](https://openreview.net/attachment?id=ElC6LYO4MfD&name=SUPP_material)] - FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy. [[PUB](https://openreview.net/forum?id=bZjxxYURKT)] [[CODE](https://openreview.net/attachment?id=bZjxxYURKT&name=SUPP_material)] - The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation. [[PUB](https://openreview.net/forum?id=29V3AWjVAFi)] [[PDF](https://arxiv.org/abs/2301.08968)] [[CODE](https://openreview.net/attachment?id=29V3AWjVAFi&name=SUPP_material)] - PerFedMask: Personalized Federated Learning with Optimized Masking Vectors. [[PUB](https://openreview.net/forum?id=hxEIgUXLFF)] [[CODE](https://github.com/MehdiSet/PerFedMask)] - EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. [[PUB](https://openreview.net/forum?id=ytZIYmztET)] [[CODE](https://github.com/MingruiLiu-ML-Lab/episode)] - FedDAR: Federated Domain-Aware Representation Learning. [[PUB](https://openreview.net/forum?id=6P9Y25Pljl6)] [[PDF](https://arxiv.org/abs/2209.04007)] [[CODE](https://github.com/zlz0414/FedDAR)] - Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning. [[PUB](https://openreview.net/forum?id=oJpVVGXu9i)] [[CODE](https://github.com/shenzebang/CENTAUR-Privacy-Federated-Representation-Learning)] - FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning. [[PUB](https://openreview.net/forum?id=Xo2E217_M4n)] [[PDF](https://arxiv.org/abs/2210.12873)] [[CODE](https://github.com/KaiyuanZh/FLIP)] - Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses. [[PUB](https://openreview.net/forum?id=-EHqoysUYLx)] - Efficient Federated Domain Translation. [[PUB](https://openreview.net/forum?id=uhLAcrAZ9cJ)] [[CODE](https://openreview.net/attachment?id=uhLAcrAZ9cJ&name=SUPP_material)] - On the Importance and Applicability of Pre-Training for Federated Learning. [[PUB](https://openreview.net/forum?id=fWWFv--P0xP)] [[PDF](https://arxiv.org/abs/2206.11488)] [[CODE](https://github.com/andytu28/fps_pre-training)] - Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. [[PUB](https://openreview.net/forum?id=r0BrY4BiEXO)] [[PDF](https://arxiv.org/abs/2201.12675)] [[CODE](https://github.com/JonasGeiping/breaching)] - A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. [[PUB](https://openreview.net/forum?id=FUiDMCr_W4o)] [[PDF](https://arxiv.org/abs/2207.01771)] - Instance-wise Batch Label Restoration via Gradients in Federated Learning. [[PUB](https://openreview.net/forum?id=FIrQfNSOoTr)] [[CODE](https://github.com/BUAA-CST/iLRG)] - Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity. [[PUB](https://openreview.net/forum?id=_hb4vM3jspB)] - CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning. [[PUB](https://openreview.net/forum?id=Kf7Yyf4O0u)] [[PDF](https://arxiv.org/abs/2210.02912)] [[CODE](https://github.com/facebookresearch/canife)] - Sparse Random Networks for Communication-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=k1FHgri5y3-)] [[PDF](https://arxiv.org/abs/2209.15328)] [[CODE](https://openreview.net/attachment?id=k1FHgri5y3-&name=SUPP_material)] - Combating Exacerbated Heterogeneity for Robust Decentralized Models. [[PUB](https://openreview.net/forum?id=eKllxpLOOm)] [[CODE](https://github.com/ZFancy/SFAT)] - Hyperparameter Optimization through Neural Network Partitioning. [[PUB](https://openreview.net/forum?id=nAgdXgfmqj)] [[PDF](https://arxiv.org/abs/2304.14766)] - Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?. [[PUB](https://openreview.net/forum?id=2L9gzS80tA4)] [[PDF](https://arxiv.org/abs/2210.10947)] [[CODE](https://openreview.net/attachment?id=2L9gzS80tA4&name=SUPP_material)] - Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. [[PUB](https://openreview.net/forum?id=pfuqQQCB34)] [[PDF](https://arxiv.org/abs/2206.00529)] [[CODE](https://github.com/SamuelHorvath/VR_Byzantine)] - Dual Diffusion Implicit Bridges for Image-to-Image Translation. [[PUB](https://openreview.net/forum?id=5HLoTvVGDe)] [[PDF](https://arxiv.org/abs/2203.08382)] [[CODE](https://openreview.net/attachment?id=5HLoTvVGDe&name=SUPP_material)] - Bias Propagation in Federated Learning. [[PUB](https://openreview.net/pdf?id=V7CYzdruWdm)] - Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. [[PUB](https://openreview.net/pdf?id=eKllxpLOOm)] - Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. [[PUB](https://openreview.net/pdf?id=Mpa3tRJFBb)] - Dataless Knowledge Fusion by Merging Weights of Language Models. [[PUB](https://openreview.net/forum?id=FCnohuR6AnM)] - DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation. [[PUB](https://openreview.net/forum?id=I_YZANaz5X)] - Git Re-Basin: Merging Models modulo Permutation Symmetries. [[PUB](https://openreview.net/forum?id=CQsmMYmlP5T)] - TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations. [[PUB](https://openreview.net/forum?id=EIgLnNx_lC)] - Why (and When) does Local SGD Generalize Better than SGD?. [[PUB](https://openreview.net/forum?id=svCcui6Drl)] ### 2022 #### colt - Statistical Estimation and Online Inference via Local SGD. [[PUB](https://proceedings.mlr.press/v178/li22b.html)] #### Mach Learn - An accurate, scalable and verifiable protocol for federated differentially private averaging. [[PUB](https://link.springer.com/article/10.1007/s10994-022-06267-9)] [[PDF](https://arxiv.org/abs/2006.07218)] #### machine learning - An accurate, scalable and verifiable protocol for federated differentially private averaging. [[PUB](https://doi.org/10.1007/s10994-022-06267-9)] #### UAI - Federated online clustering of bandits. [[PUB](https://openreview.net/forum?id=rKUgiU8iqeq)] [[PDF](https://arxiv.org/abs/2208.14865)] [[CODE](https://github.com/zhaohaoru/federated-clustering-of-bandits)] - Privacy-aware compression for federated data analysis. [[PUB](https://openreview.net/forum?id=BqUdRP8i9e9)] [[PDF](https://arxiv.org/abs/2203.08134)] [[CODE](https://github.com/facebookresearch/dp_compression)] - Faster non-convex federated learning via global and local momentum. [[PUB](https://openreview.net/forum?id=SSlLRUIs9e9)] [[PDF](https://arxiv.org/abs/2012.04061)] - Fedvarp: Tackling the variance due to partial client participation in federated learning. [[PUB](https://openreview.net/forum?id=HlWLLdUocx5)] [[PDF](https://arxiv.org/abs/2207.14130)] - SASH: Efficient secure aggregation based on SHPRG for federated learning. [[PUB](https://openreview.net/forum?id=HSleBPIoql9)] [[PDF](https://arxiv.org/abs/2111.12321)] - Bayesian federated estimation of causal effects from observational data. [[PUB](https://openreview.net/forum?id=BEl3vP8sqlc)] [[PDF](https://arxiv.org/abs/2106.00456)] #### TPAMI - Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/9625795)] - Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/9238427)] [[CODE](https://github.com/sunjunaimer/TPAMI-LAQ)] - Collaborative Learning of Label Semantics and Deep Label-Specific Features for Multi-Label Classification. [[PUB](https://doi.org/10.1109/TPAMI.2021.3136592)] #### NeurIPS - Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox. [[PUB](https://openreview.net/forum?id=W72rB0wwLVu)] [[PDF](https://arxiv.org/abs/2207.03957)] - LAMP: Extracting Text from Gradients with Language Model Priors. [[PUB](https://openreview.net/forum?id=6iqd9JAVR1z)] [[CODE](https://openreview.net/attachment?id=6iqd9JAVR1z&name=SUPP_material)] - FedAvg with Fine Tuning: Local Updates Lead to Representation Learning. [[PUB](https://openreview.net/forum?id=G3fswMh9P8y)] [[PDF](https://arxiv.org/abs/2205.13692)] - On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond. [[PUB](https://openreview.net/forum?id=_33ynl9VgCX)] [[PDF](https://arxiv.org/abs/2206.05187)] - Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. [[PUB](https://openreview.net/forum?id=i9XrHJoyLqJ)] [[CODE](https://openreview.net/attachment?id=i9XrHJoyLqJ&name=SUPP_material)] - Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks. [[PUB](https://openreview.net/forum?id=Vj-jYs47cx)] [[PDF](https://arxiv.org/abs/2206.10870)] - Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. [[PUB](https://openreview.net/forum?id=wo-a8Ji6s3A)] - Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. [[PUB](https://openreview.net/forum?id=mUeMOdJ2IJp)] [[PDF](https://arxiv.org/abs/2210.13497)] - EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. [[PUB](https://openreview.net/forum?id=PeJO709WUup)] [[PDF](https://arxiv.org/abs/2205.04180)] - On-Demand Sampling: Learning Optimally from Multiple Distributions. [[PUB](https://openreview.net/forum?id=FR289LMkmxZ)] [[CODE](https://openreview.net/attachment?id=FR289LMkmxZ&name=SUPP_material)] - Improved Utility Analysis of Private CountSketch. [[PUB](https://openreview.net/forum?id=XFCirHGr4Cs)] [[PDF](https://arxiv.org/abs/2205.08397)] [[CODE](https://github.com/rasmus-pagh/private-countsketch)] - Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning. [[PUB](https://openreview.net/forum?id=APXedc0hgdT)] [[CODE](https://openreview.net/attachment?id=APXedc0hgdT&name=SUPP_material)] - Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. [[PUB](https://openreview.net/forum?id=Y4vT7m4e3d)] [[PDF](https://arxiv.org/abs/2106.08315)] - BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression. [[PUB](https://openreview.net/forum?id=I47eFCKa1f3)] [[PDF](https://arxiv.org/abs/2201.13320)] [[CODE](https://github.com/liboyue/beer)] - Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning. [[PUB](https://openreview.net/forum?id=KOHC_CYEIuP)] [[PDF](https://arxiv.org/abs/2202.06083)] - Near-Optimal Collaborative Learning in Bandits. [[PUB](https://openreview.net/forum?id=2xfJ26BuFP)] [[PDF](https://arxiv.org/abs/2206.00121)] [[CODE](https://github.com/clreda/near-optimal-federated)] - Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. [[PUB](https://openreview.net/forum?id=J0nhRuMkdGf)] [[PDF](https://arxiv.org/abs/2110.03313)] - Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. [[PUB](https://openreview.net/forum?id=SNElc7QmMDe)] [[CODE](https://openreview.net/attachment?id=SNElc7QmMDe&name=SUPP_material)] - FedPop: A Bayesian Approach for Personalised Federated Learning. [[PUB](https://openreview.net/forum?id=KETwimTQexH)] [[PDF](https://arxiv.org/abs/2206.03611)] - Fairness in Federated Learning via Core-Stability. [[PUB](https://openreview.net/forum?id=lKULHf7oFDo)] [[CODE](https://openreview.net/attachment?id=lKULHf7oFDo&name=SUPP_material)] - SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning. [[PUB](https://openreview.net/forum?id=25XIE30VHZE)] [[PDF](https://arxiv.org/abs/2210.01639)] - FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel Extraction. [[PUB](https://openreview.net/forum?id=OtxyysUdBE)] [[CODE](https://github.com/MSU-MLSys-Lab/FedRolex)] - On Sample Optimality in Personalized Collaborative and Federated Learning. [[PUB](https://openreview.net/forum?id=7EP90NMAoK)] - DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing. [[PUB](https://openreview.net/forum?id=hPkGV4BPsmv)] [[PDF](https://arxiv.org/abs/2210.02680)] - FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning. [[PUB](https://openreview.net/forum?id=5vVSA_cdRqe)] - Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. [[PUB](https://openreview.net/forum?id=edkno3SvKo)] [[PDF](https://arxiv.org/abs/2207.04338)] - VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?. [[PUB](https://openreview.net/forum?id=vNrSXIFJ9wz)] [[CODE](https://openreview.net/attachment?id=edkno3SvKo&name=SUPP_material)] - DENSE: Data-Free One-Shot Federated Learning. [[PUB](https://openreview.net/forum?id=QFQoxCFYEkA)] [[PDF](https://arxiv.org/abs/2112.12371)] - CalFAT: Calibrated Federated Adversarial Training with Label Skewness. [[PUB](https://openreview.net/forum?id=8N1NDRGQSQ)] [[PDF](https://arxiv.org/abs/2205.14926)] - SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning. [[PUB](https://openreview.net/forum?id=wTp4KgVIJ5)] [[PDF](https://arxiv.org/abs/2210.00611)] - Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning. [[PUB](https://openreview.net/forum?id=8SilFGuXgmk)] [[PDF](https://arxiv.org/abs/2210.00690)] - Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. [[PUB](https://openreview.net/forum?id=wFymjzZEEkH)] - Federated Submodel Optimization for Hot and Cold Data Features. [[PUB](https://openreview.net/forum?id=sj9l1JCrAk6)] - BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. [[PUB](https://openreview.net/forum?id=jzd2bE5MxW)] [[PDF](https://arxiv.org/abs/2207.06343)] - Byzantine-tolerant federated Gaussian process regression for streaming data. [[PUB](https://openreview.net/forum?id=Nx4gNemvNvx)] [[CODE](https://openreview.net/attachment?id=Nx4gNemvNvx&name=SUPP_material)] - SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression. [[PUB](https://openreview.net/forum?id=tz1PRT6lfLe)] [[PDF](https://arxiv.org/abs/2206.09888)] - Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering. [[PUB](https://openreview.net/forum?id=N0tKCpMhA2)] [[PDF](https://arxiv.org/abs/2210.14664)] [[CODE](https://github.com/haoyuzhao123/coreset-vfl-codes)] - Communication Efficient Federated Learning for Generalized Linear Bandits. [[PUB](https://openreview.net/forum?id=Xwz9B6LDM5c)] [[CODE](https://openreview.net/attachment?id=Xwz9B6LDM5c&name=SUPP_material)] - Recovering Private Text in Federated Learning of Language Models. [[PUB](https://openreview.net/forum?id=dqgzfhHd2-)] [[PDF](https://arxiv.org/abs/2205.08514)] [[CODE](https://github.com/Princeton-SysML/FILM)] - Federated Learning from Pre-Trained Models: A Contrastive Learning Approach. [[PUB](https://openreview.net/forum?id=mhQLcMjWw75)] [[PDF](https://arxiv.org/abs/2209.10083)] - Global Convergence of Federated Learning for Mixed Regression. [[PUB](https://openreview.net/forum?id=DdxNka9tMRd)] [[PDF](https://arxiv.org/abs/2206.07279)] - Resource-Adaptive Federated Learning with All-In-One Neural Composition. [[PUB](https://openreview.net/forum?id=wfel7CjOYk)] - Self-Aware Personalized Federated Learning. [[PUB](https://openreview.net/forum?id=EqJ5_hZSqgy)] [[PDF](https://arxiv.org/abs/2204.08069)] - A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning. [[PUB](https://openreview.net/forum?id=TATzsweWfof)] [[PDF](https://arxiv.org/abs/2206.01132)] - An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects. [[PUB](https://openreview.net/forum?id=fJt2KFnRqZ)] - Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. [[PUB](https://openreview.net/forum?id=4_oCZgBIVI)] [[PDF](https://arxiv.org/abs/2206.08307)] - Personalized Online Federated Multi-Kernel Learning. [[PUB](https://openreview.net/forum?id=wUctlvhsNWg)] - SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. [[PUB](https://openreview.net/forum?id=1GAjC_FauE)] [[PDF](https://arxiv.org/abs/2106.01432)] [[CODE](https://openreview.net/attachment?id=1GAjC_FauE&name=SUPP_material)] - A Unified Analysis of Federated Learning with Arbitrary Client Participation. [[PUB](https://openreview.net/forum?id=qSs7C7c4G8D)] [[PDF](https://arxiv.org/abs/2205.13648)] - Preservation of the Global Knowledge by Not-True Distillation in Federated Learning. [[PUB](https://openreview.net/forum?id=qw3MZb1Juo)] [[PDF](https://arxiv.org/abs/2106.03097)] [[CODE](https://openreview.net/attachment?id=qw3MZb1Juo&name=SUPP_material)] - FedSR: A Simple and Effective Domain Generalization Method for Federated Learning. [[PUB](https://openreview.net/forum?id=mrt90D00aQX)] [[CODE](https://openreview.net/attachment?id=mrt90D00aQX&name=SUPP_material)] - Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching. [[PUB](https://openreview.net/forum?id=Ql75oqz1npy)] [[PDF](https://arxiv.org/abs/2202.00270)] [[CODE](https://github.com/wyjeong/Factorized-FL)] - A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. [[PUB](https://openreview.net/forum?id=Fx7oXUVEPW)] [[PDF](https://arxiv.org/abs/2207.03106)] - Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework. [[PUB](https://openreview.net/forum?id=4OHRr7gmhd4)] - On Privacy and Personalization in Cross-Silo Federated Learning. [[PUB](https://openreview.net/forum?id=Oq2bdIQQOIZ)] [[PDF](https://arxiv.org/abs/2206.07902)] - A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning. [[PUB](https://openreview.net/forum?id=fiBnhdazkyx)] [[PDF](https://arxiv.org/abs/2106.06312)] [[CODE](https://github.com/Xtra-Computing/FedSim)] - Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/e7feb9dbd9a94b6c552fc403fcebf2ef-Abstract-Conference.html)] [[CODE](https://github.com/wyjeong/Factorized-FL)] - FLAIR: Federated Learning Annotated Image Repository. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/f64e55d03e2fe61aa4114e49cb654acb-Abstract-Datasets_and_Benchmarks.html)] - FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/232eee8ef411a0a316efa298d7be3c2b-Abstract-Datasets_and_Benchmarks.html)] - Personalized Online Federated Learning with Multiple Kernels. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/d78cc4e15f8fbdb0dd77e551601f572c-Abstract-Conference.html)] - pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/3cc03e19fed71a2b9347d83921ca2e7d-Abstract-Datasets_and_Benchmarks.html)] - TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/c7649eeb93d2fad0ced9a3b974260710-Abstract-Conference.html)] - A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/a7fa0a0d6b4bb14c659b9921e8e4a772-Abstract-Conference.html)] - Collaborative Learning by Detecting Collaboration Partners. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/646ca7b994bc46afe33d680dbe7ed67a-Abstract-Conference.html)] - Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/cf326db238429dac58625977f6fb8265-Abstract-Conference.html)] - Communication Efficient Distributed Learning for Kernelized Contextual Bandits. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/7d1043b688002734b49b766cc2fc478d-Abstract-Conference.html)] - Communication-efficient distributed eigenspace estimation with arbitrary node failures. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/73b038fffc99ae11056e936f9a299508-Abstract-Conference.html)] - GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/4d6938f94ab47d32128c239a4bfedae0-Abstract-Conference.html)] - Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/2616697705f72f16a8eac9c295d37d94-Abstract-Conference.html)] - Merging Models with Fisher-Weighted Averaging. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/70c26937fbf3d4600b69a129031b66ec-Abstract-Conference.html)] - The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/109cf25cbc36037deecdbeabfa199956-Abstract-Conference.html)] - Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/c50c42f853db0f1f5b4195358b6d97de-Abstract-Conference.html)] - SAGDA: Achieving $\mathcal{O}(\epsilon{-2})$ Communication Complexity in Federated Min-Max Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/2f13806d6580db60d9d7d6f89ba529ca-Abstract-Conference.html)] - Taming Fat-Tailed ("Heavier-Tailed" with Potentially Infinite Variance) Noise in Federated Learning. [[PUB](http://papers.nips.cc/paper_files/paper/2022/hash/6cb7246003d556c4d1cbf9c17c392ee3-Abstract-Conference.html)] #### NeurIPS Datasets and Benchmarks - FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. [[PUB](https://openreview.net/forum?id=GgM5DiAb6A2)] [[CODE](https://github.com/owkin/FLamby)] #### ICML - A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. [[PUB](https://proceedings.mlr.press/v162/tan22a.html)] [[PDF](https://arxiv.org/abs/2103.06261)] [[CODE](https://github.com/ellenxtan/ifedtree)] - Fast Composite Optimization and Statistical Recovery in Federated Learning. [[PUB](https://proceedings.mlr.press/v162/bao22b.html)] [[PDF](https://arxiv.org/abs/2207.08204)] [[CODE](https://github.com/MingruiLiu-ML-Lab/Federated-Sparse-Learning)] - Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. [[PUB](https://proceedings.mlr.press/v162/bietti22a.html)] [[PDF](https://arxiv.org/abs/2202.05318)] [[CODE](https://github.com/albietz/ppsgd)] - The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning :fire:. [[PUB](https://proceedings.mlr.press/v162/chen22c.html)] [[PDF](https://arxiv.org/abs/2203.03761)] [[CODE](https://github.com/google-research/federated/tree/master/private_linear_compression)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/17529.pdf)] - The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation. [[PUB](https://proceedings.mlr.press/v162/chen22s.html)] [[PDF](https://arxiv.org/abs/2207.09916)] [[CODE](https://github.com/WeiNingChen/pbm)] - DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training. [[PUB](https://proceedings.mlr.press/v162/dai22b.html)] [[PDF](https://arxiv.org/abs/2206.00187)] [[CODE](https://github.com/rong-dai/DisPFL)] - FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning. [[PUB](https://proceedings.mlr.press/v162/elgabli22a.html)] [[PDF](https://arxiv.org/abs/2206.08829)] [[CODE](https://github.com/aelgabli/FedNew)] - DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning. [[PUB](https://proceedings.mlr.press/v162/honig22a.html)] [[PDF](https://arxiv.org/abs/2111.00465)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/16009.pdf)] [[CODE](https://media.icml.cc/Conferences/ICML2022/SUPP/honig22a-supp.zip)] - Accelerated Federated Learning with Decoupled Adaptive Optimization. [[PUB](https://proceedings.mlr.press/v162/jin22e.html)] [[PDF](https://arxiv.org/abs/2207.07223)] - Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling. [[PUB](https://proceedings.mlr.press/v162/khodadadian22a.html)] [[PDF](https://arxiv.org/abs/2206.10185)] - Multi-Level Branched Regularization for Federated Learning. [[PUB](https://proceedings.mlr.press/v162/kim22a.html)] [[PDF](https://arxiv.org/abs/2207.06936)] [[CODE](https://github.com/jinkyu032/FedMLB)] [[PAGE](http://cvlab.snu.ac.kr/research/FedMLB/)] - FedScale: Benchmarking Model and System Performance of Federated Learning at Scale :fire:. [[PUB](https://proceedings.mlr.press/v162/lai22a.html)] [[PDF](https://arxiv.org/abs/2105.11367)] [[CODE](https://github.com/SymbioticLab/FedScale)] - Federated Learning with Positive and Unlabeled Data. [[PUB](https://proceedings.mlr.press/v162/lin22b.html)] [[PDF](https://arxiv.org/abs/2106.10904)] [[CODE](https://github.com/littlesunlxy/fedpu-torch)] - Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning. [[PUB](https://proceedings.mlr.press/v162/liu22k.html)] [[CODE](https://github.com/Thinklab-SJTU/GAMF)] - Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering. [[PUB](https://proceedings.mlr.press/v162/lubana22a.html)] [[PDF](https://arxiv.org/abs/2205.11506)] [[CODE](https://github.com/akhilmathurs/orchestra)] - Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring. [[PUB](https://proceedings.mlr.press/v162/luo22b.html)] [[PDF](https://arxiv.org/abs/2206.06818)] [[CODE](https://github.com/luozhengquan/DFL)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/16881.pdf)] [[解读](https://www.bilibili.com/read/cv17092678)] - Architecture Agnostic Federated Learning for Neural Networks. [[PUB](https://proceedings.mlr.press/v162/makhija22a.html)] [[PDF](https://proceedings.mlr.press/v162/zhang22p.html)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/16926.pdf)] - Personalized Federated Learning through Local Memorization. [[PUB](https://proceedings.mlr.press/v162/marfoq22a.html)] [[PDF](https://arxiv.org/abs/2111.09360)] [[CODE](https://github.com/omarfoq/knn-per)] - Proximal and Federated Random Reshuffling. [[PUB](https://proceedings.mlr.press/v162/mishchenko22a.html)] [[PDF](https://arxiv.org/abs/2102.06704)] [[CODE](https://github.com/konstmish/rr_prox_fed)] - Federated Learning with Partial Model Personalization. [[PUB](https://proceedings.mlr.press/v162/pillutla22a.html)] [[PDF](https://arxiv.org/abs/2204.03809)] [[CODE](https://github.com/krishnap25/FL_partial_personalization)] - Generalized Federated Learning via Sharpness Aware Minimization. [[PUB](https://proceedings.mlr.press/v162/qu22a.html)] [[PDF](https://arxiv.org/abs/2206.02618)] - FedNL: Making Newton-Type Methods Applicable to Federated Learning. [[PUB](https://proceedings.mlr.press/v162/safaryan22a.html)] [[PDF](https://arxiv.org/abs/2106.02969)] [[VIDEO](https://www.youtube.com/watch?v=_VYCEWT17R0&ab_channel=FederatedLearningOneWorldSeminar)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/17084.pdf)] - Federated Minimax Optimization: Improved Convergence Analyses and Algorithms. [[PUB](https://proceedings.mlr.press/v162/sharma22c.html)] [[PDF](https://arxiv.org/abs/2203.04850)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/17435.pdf)] - Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. [[PUB](https://proceedings.mlr.press/v162/tang22d.html)] [[PDF](https://arxiv.org/abs/2206.02465)] [[CODE](https://github.com/wizard1203/VHL)] [[解读](https://zhuanlan.zhihu.com/p/548508633)] - FedNest: Federated Bilevel, Minimax, and Compositional Optimization. [[PUB](https://proceedings.mlr.press/v162/tarzanagh22a.html)] [[PDF](https://arxiv.org/abs/2205.02215)] [[CODE](https://github.com/ucr-optml/FedNest)] - EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning. [[PUB](https://proceedings.mlr.press/v162/vargaftik22a.html)] [[PDF](https://arxiv.org/abs/2108.08842)] [[CODE](https://github.com/amitport/EDEN-Distributed-Mean-Estimation)] - Communication-Efficient Adaptive Federated Learning. [[PUB](https://proceedings.mlr.press/v162/wang22o.html)] [[PDF](https://arxiv.org/abs/2205.02719)] - ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. [[PUB](https://proceedings.mlr.press/v162/wang22y.html)] [[PDF](https://arxiv.org/abs/2110.05323)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/16194_hmjFNsN.pdf)] [[CODE](https://github.com/a514514772/ProgFed)] - Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification :fire:. [[PUB](https://proceedings.mlr.press/v162/wen22a.html)] [[PDF](https://arxiv.org/abs/2202.00580)] [[CODE](https://github.com/JonasGeiping/breaching)] - Anarchic Federated Learning. [[PUB](https://proceedings.mlr.press/v162/yang22r.html)] [[PDF](https://arxiv.org/abs/2108.09875)] - QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning. [[PUB](https://proceedings.mlr.press/v162/yi22a.html)] [[CODE](https://github.com/LipingYi/QSFL)] - Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization. [[PUB](https://proceedings.mlr.press/v162/yoon22a.html)] [[PDF](https://arxiv.org/abs/2202.11453)] - Neural Tangent Kernel Empowered Federated Learning. [[PUB](https://proceedings.mlr.press/v162/yue22a.html)] [[PDF](https://arxiv.org/abs/2110.03681)] [[CODE](https://github.com/KAI-YUE/ntk-fed)] - Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy. [[PUB](https://proceedings.mlr.press/v162/zhang22b.html)] [[PDF](https://arxiv.org/abs/2106.13673)] - Personalized Federated Learning via Variational Bayesian Inference. [[PUB](https://proceedings.mlr.press/v162/zhang22o.html)] [[PDF](https://arxiv.org/abs/2206.07977)] [[SLIDE](https://icml.cc/media/icml-2022/Slides/17302.pdf)] [[UC.](https://github.com/AllenBeau/pFedBayes)] - Federated Learning with Label Distribution Skew via Logits Calibration. [[PUB](https://proceedings.mlr.press/v162/zhang22p.html)] - Neurotoxin: Durable Backdoors in Federated Learning. [[PUB](https://proceedings.mlr.press/v162/zhang22w.html)] [[PDF](https://arxiv.org/abs/2206.10341)] [[CODE](https://github.com/jhcknzzm/Federated-Learning-Backdoor/)] - Resilient and Communication Efficient Learning for Heterogeneous Federated Systems. [[PUB](https://proceedings.mlr.press/v162/zhu22e.html)] - FedScale: Benchmarking Model and System Performance of Federated Learning at Scale. [[PUB](https://proceedings.mlr.press/v162/lai22a.html)] - Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. [[PUB](https://proceedings.mlr.press/v162/wen22a.html)] - The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. [[PUB](https://proceedings.mlr.press/v162/chen22c.html)] - 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. [[PUB](https://proceedings.mlr.press/v162/richtarik22a.html)] - Communication-efficient Distributed Learning for Large Batch Optimization. [[PUB](https://proceedings.mlr.press/v162/liu22n.html)] - PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. [[PUB](https://proceedings.mlr.press/v162/li22s.html)] [[CODE](https://github.com/DIG-Beihang/MIR3)] #### ICLR (oral) - Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. [[PUB](https://openreview.net/forum?id=LdlwbBP2mlq)] [[CODE](https://openreview.net/attachment?id=LdlwbBP2mlq&name=SUPP_material)] #### ICLR - Bayesian Framework for Gradient Leakage. [[PUB](https://openreview.net/forum?id=f2lrIbGx3x7)] [[PDF](https://arxiv.org/abs/2111.04706)] [[CODE](https://github.com/eth-sri/bayes-framework-leakage)] - Federated Learning from only unlabeled data with class-conditional-sharing clients. [[PUB](https://openreview.net/forum?id=WHA8009laxu)] [[CODE](https://github.com/lunanbit/FedUL)] - FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning. [[PUB](https://openreview.net/forum?id=ZaVVVlcdaN)] [[PDF](https://arxiv.org/abs/2108.06869.)] - Acceleration of Federated Learning with Alleviated Forgetting in Local Training. [[PUB](https://openreview.net/forum?id=541PxiEKN3F)] [[PDF](https://arxiv.org/abs/2203.02645)] [[CODE](https://github.com/Zoesgithub/FedReg)] - FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=d71n4ftoCBy)] [[PDF](https://arxiv.org/abs/2108.06098)] [[CODE](https://github.com/South-hw/FedPara_ICLR22)] - An Agnostic Approach to Federated Learning with Class Imbalance. [[PUB](https://openreview.net/forum?id=Xo0lbDt975)] [[CODE](https://github.com/shenzebang/Federated-Learning-Pytorch)] - Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization. [[PUB](https://openreview.net/forum?id=_QLmakITKg)] [[PDF](https://arxiv.org/abs/2203.09747)] [[CODE](https://github.com/illidanlab/SplitMix)] - Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models :fire:. [[PUB](https://openreview.net/forum?id=fwzUgo0FM9v)] [[PDF](https://arxiv.org/abs/2110.13057)] [[CODE](https://github.com/JonasGeiping/breaching)] - ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. [[PUB](https://openreview.net/forum?id=2sDQwC_hmnM)] [[PDF](https://arxiv.org/abs/2208.02507)] - Diverse Client Selection for Federated Learning via Submodular Maximization. [[PUB](https://openreview.net/forum?id=nwKXyFvaUm)] [[CODE](https://github.com/melodi-lab/divfl)] - Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?. [[PUB](https://openreview.net/forum?id=B7ZbqNLDn-_)] [[PDF](https://arxiv.org/abs/2202.00280)] [[CODE](https://github.com/shams-sam/FedOptim)] - Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions :fire:. [[PUB](https://openreview.net/forum?id=E4EE_ohFGz)] [[CODE](https://github.com/google-research/federated/tree/7525c36324cb022bc05c3fce88ef01147cae9740/periodic_distribution_shift)] - Towards Model Agnostic Federated Learning Using Knowledge Distillation. [[PUB](https://openreview.net/forum?id=lQI_mZjvBxj)] [[PDF](https://arxiv.org/abs/2110.15210)] [[CODE](https://github.com/AfoninAndrei/ICLR2022)] - Divergence-aware Federated Self-Supervised Learning. [[PUB](https://openreview.net/forum?id=oVE1z8NlNe)] [[PDF](https://arxiv.org/abs/2204.04385)] [[CODE](https://github.com/EasyFL-AI/EasyFL)] - What Do We Mean by Generalization in Federated Learning? :fire:. [[PUB](https://openreview.net/forum?id=VimqQq-i_Q)] [[PDF](https://arxiv.org/abs/2110.14216)] [[CODE](https://github.com/google-research/federated/tree/master/generalization)] - FedBABU: Toward Enhanced Representation for Federated Image Classification. [[PUB](https://openreview.net/forum?id=HuaYQfggn5u)] [[PDF](https://arxiv.org/abs/2106.06042)] [[CODE](https://github.com/jhoon-oh/FedBABU)] - Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. [[PUB](https://openreview.net/forum?id=jXKKDEi5vJt)] [[PDF](https://arxiv.org/abs/2006.09365)] [[CODE](https://github.com/liehe/byzantine-robust-noniid-optimizer)] - Hybrid Local SGD for Federated Learning with Heterogeneous Communications. [[PUB](https://openreview.net/forum?id=H0oaWl6THa)] - On Bridging Generic and Personalized Federated Learning for Image Classification. [[PUB](https://openreview.net/forum?id=I1hQbx10Kxn)] [[PDF](https://arxiv.org/abs/2107.00778)] [[CODE](https://github.com/hongyouc/Fed-RoD)] - Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. [[PUB](https://openreview.net/forum?id=LdlwbBP2mlq)] [[PDF](https://arxiv.org/abs/2110.10342)] - Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. [[PUB](https://openreview.net/forum?id=E4EE_ohFGz)] - FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning. [[PUB](https://openreview.net/forum?id=d71n4ftoCBy)] - Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters. [[PUB](https://openreview.net/forum?id=7l1IjZVddDW)] - Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. [[PUB](https://openreview.net/forum?id=fwzUgo0FM9v)] - What Do We Mean by Generalization in Federated Learning?. [[PUB](https://openreview.net/forum?id=VimqQq-i_Q)] - SGD Can Converge to Local Maxima. [[PUB](https://openreview.net/forum?id=9XhPLAjjRB)] #### ICLR Spotlight - Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters. [[PUB](https://openreview.net/forum?id=7l1IjZVddDW)] [[PDF](https://arxiv.org/abs/2201.12467)] [[PAGE](https://irvingmeng.github.io/projects/privacyface/)] [[解读](https://zhuanlan.zhihu.com/p/484920301)] ### 2021 #### JMLR - One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. [[PUB](http://jmlr.org/papers/v22/19-1048.html)] [[CODE](https://github.com/sabersalehk/MRE_C)] - Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA. [[PUB](https://jmlr.org/papers/v22/20-705.html)] - Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms. [[PUB](https://jmlr.org/papers/v22/20-147.html)] - Estimating Uncertainty Intervals from Collaborating Networks. [[PUB](https://jmlr.org/papers/v22/20-1100.html)] - FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection. [[PUB](https://jmlr.org/papers/v22/20-815.html)] - Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model. [[PUB](https://jmlr.org/papers/v22/19-325.html)] #### tpami - Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction. [[PUB](https://doi.org/10.1109/TPAMI.2020.2991344)] #### UAI - Constrained differentially private federated learning for low-bandwidth devices. [[PUB](https://proceedings.mlr.press/v161/kerkouche21a.html)] [[PDF](https://arxiv.org/abs/2103.00342)] - Federated stochastic gradient Langevin dynamics. [[PUB](https://proceedings.mlr.press/v161/mekkaoui21a.html)] [[PDF](https://arxiv.org/abs/2004.11231)] #### ICLR - Federated Learning Based on Dynamic Regularization. [[PUB](https://openreview.net/forum?id=B7v4QMR6Z9w)] [[PDF](https://arxiv.org/abs/2111.04263)] [[CODE](https://github.com/AntixK/FedDyn)] - Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning. [[PUB](https://openreview.net/forum?id=jDdzh5ul-d)] [[PDF](https://arxiv.org/abs/2101.11203)] - HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. [[PUB](https://openreview.net/forum?id=TNkPBBYFkXg)] [[PDF](https://arxiv.org/abs/2010.01264)] [[CODE](https://github.com/dem123456789/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients)] - FedMix: Approximation of Mixup under Mean Augmented Federated Learning. [[PUB](https://openreview.net/forum?id=Ogga20D2HO-)] [[PDF](https://arxiv.org/abs/2107.00233)] - Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms :fire:. [[PUB](https://openreview.net/forum?id=GFsU8a0sGB)] [[PDF](https://arxiv.org/abs/2010.05273)] [[CODE](https://github.com/alshedivat/fedpa)] - Adaptive Federated Optimization :fire:. [[PUB](https://openreview.net/forum?id=LkFG3lB13U5)] [[PDF](https://arxiv.org/abs/2003.00295)] [[CODE](https://github.com/google-research/federated/tree/master/optimization)] - Personalized Federated Learning with First Order Model Optimization. [[PUB](https://openreview.net/forum?id=ehJqJQk9cw)] [[PDF](https://arxiv.org/abs/2012.08565)] [[CODE](https://github.com/NVlabs/FedFomo)] [[UC.](https://github.com/TsingZ0/PFL-Non-IID)] - FedBN: Federated Learning on Non-IID Features via Local Batch Normalization :fire:. [[PUB](https://openreview.net/forum?id=6YEQUn0QICG)] [[PDF](https://arxiv.org/abs/2102.07623)] [[CODE](https://github.com/med-air/FedBN)] - FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning. [[PUB](https://openreview.net/forum?id=dgtpE6gKjHn)] [[PDF](https://arxiv.org/abs/2009.01974)] [[CODE](https://github.com/hongyouc/fedbe)] - Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning. [[PUB](https://openreview.net/forum?id=ce6CFXBh30h)] [[PDF](https://arxiv.org/abs/2006.12097)] [[CODE](https://github.com/wyjeong/FedMatch)] - Adaptive Federated Optimization. [[PUB](https://openreview.net/forum?id=LkFG3lB13U5)] - FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. [[PUB](https://openreview.net/forum?id=6YEQUn0QICG)] [[CODE](https://github.com/med-air/FedBN)] - Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning. [[PUB](https://openreview.net/forum?id=ce6CFXBh30h)] [[CODE](https://github.com/wyjeong/FedMatch)] - A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. [[PUB](https://openreview.net/forum?id=vYVI1CHPaQg)] - CaPC Learning: Confidential and Private Collaborative Learning. [[PUB](https://openreview.net/forum?id=h2EbJ4_wMVq)] - Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks. [[PUB](https://openreview.net/forum?id=C70cp4Cn32)] - Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. [[PUB](https://openreview.net/forum?id=GFsU8a0sGB)] #### ICML - KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation. [[PUB](http://proceedings.mlr.press/v139/feng21f.html)] [[PDF](https://arxiv.org/abs/2011.09757)] [[CODE](https://github.com/FengHZ/KD3A)] [[解读](https://mp.weixin.qq.com/s/gItgiZmKUxg0ltaeOVdnRw)] - Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. [[PUB](http://proceedings.mlr.press/v139/lam21b.html)] [[PDF](https://arxiv.org/abs/2106.06089)] [[VIDEO](https://slideslive.com/38958558/gradient-disaggregation-breaking-privacy-in-federated-learning-by-reconstructing-the-user-participant-matrix)] [[CODE](https://github.com/gdisag/gradient_disaggregation)] - FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis. [[PUB](http://proceedings.mlr.press/v139/huang21c.html)] [[PDF](https://arxiv.org/abs/2105.05001)] [[VIDEO](https://slideslive.com/38959650/flntk-a-neural-tangent-kernelbased-framework-for-federated-learning-analysis)] - Personalized Federated Learning using Hypernetworks :fire:. [[PUB](http://proceedings.mlr.press/v139/shamsian21a.html)] [[PDF](https://arxiv.org/abs/2103.04628)] [[CODE](https://github.com/AvivSham/pFedHN)] [[PAGE](https://avivsham.github.io/pfedhn/)] [[VIDEO](https://slideslive.com/38959583/personalized-federated-learning-using-hypernetworks)] [[解读](https://zhuanlan.zhihu.com/p/431130945)] - Federated Composite Optimization. [[PUB](http://proceedings.mlr.press/v139/yuan21d.html)] [[PDF](https://arxiv.org/abs/2011.08474)] [[CODE](https://github.com/hongliny/FCO-ICML21)] [[VIDEO](https://www.youtube.com/watch?v=tKDbc60XJks&ab_channel=FederatedLearningOneWorldSeminar)] [[SLIDE](https://hongliny.github.io/files/FCO_ICML21/FCO_ICML21_slides.pdf)] - Exploiting Shared Representations for Personalized Federated Learning. [[PUB](http://proceedings.mlr.press/v139/collins21a.html)] [[PDF](https://arxiv.org/abs/2102.07078)] [[CODE](https://github.com/lgcollins/FedRep)] [[VIDEO](https://slideslive.com/38959519/exploiting-shared-representations-for-personalized-federated-learning)] - Data-Free Knowledge Distillation for Heterogeneous Federated Learning :fire:. [[PUB](http://proceedings.mlr.press/v139/zhu21b.html)] [[PDF](https://arxiv.org/abs/2105.10056)] [[CODE](https://github.com/zhuangdizhu/FedGen)] [[VIDEO](https://slideslive.com/38959429/datafree-knowledge-distillation-for-heterogeneous-federated-learning)] - Federated Continual Learning with Weighted Inter-client Transfer. [[PUB](http://proceedings.mlr.press/v139/yoon21b.html)] [[PDF](https://arxiv.org/abs/2003.03196)] [[CODE](https://github.com/wyjeong/FedWeIT)] [[VIDEO](https://slideslive.com/38959323/federated-continual-learning-with-weighted-interclient-transfer)] - Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. [[PUB](http://proceedings.mlr.press/v139/yuan21a.html)] [[PDF](https://arxiv.org/abs/2102.04635)] [[CODE](https://libauc.org/)] [[VIDEO](https://slideslive.com/38959235/federated-deep-auc-maximization-for-hetergeneous-data-with-a-constant-communication-complexity)] - Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning. [[PUB](http://proceedings.mlr.press/v139/murata21a.html)] [[PDF](https://arxiv.org/abs/2102.03198)] [[VIDEO](https://slideslive.com/38959169/biasvariance-reduced-local-sgd-for-less-heterogeneous-federated-learning)] - Federated Learning of User Verification Models Without Sharing Embeddings. [[PUB](http://proceedings.mlr.press/v139/hosseini21a.html)] [[PDF](https://arxiv.org/abs/2104.08776)] [[VIDEO](https://slideslive.com/38958858/federated-learning-of-user-verification-models-without-sharing-embeddings)] - Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. [[PUB](http://proceedings.mlr.press/v139/fraboni21a.html)] [[PDF](https://arxiv.org/abs/2105.05883)] [[CODE](https://github.com/Accenture//Labs-Federated-Learning/tree/clustered_sampling)] [[VIDEO](https://slideslive.com/38959618/clustered-sampling-lowvariance-and-improved-representativity-for-clients-selection-in-federated-learning)] - Ditto: Fair and Robust Federated Learning Through Personalization. [[PUB](http://proceedings.mlr.press/v139/li21h.html)] [[PDF](https://arxiv.org/abs/2012.04221)] [[CODE](https://github.com/litian96/ditto)] [[VIDEO](https://slideslive.com/38955195/ditto-fair-and-robust-federated-learning-through-personalization)] - Heterogeneity for the Win: One-Shot Federated Clustering. [[PUB](http://proceedings.mlr.press/v139/dennis21a.html)] [[PDF](https://arxiv.org/abs/2103.00697)] [[VIDEO](https://slideslive.com/38959380/heterogeneity-for-the-win-oneshot-federated-clustering)] - The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation :fire:. [[PUB](http://proceedings.mlr.press/v139/kairouz21a.html)] [[PDF](https://arxiv.org/abs/2102.06387)] [[CODE](https://github.com/google-research/federated/tree/master/distributed_dp)] [[VIDEO](https://slideslive.com/38959306/the-distributed-discrete-gaussian-mechanism-for-federated-learning-with-secure-aggregation)] - Debiasing Model Updates for Improving Personalized Federated Training. [[PUB](http://proceedings.mlr.press/v139/acar21a.html)] [[CODE](https://github.com/venkatesh-saligrama/Personalized-Federated-Learning)] [[VIDEO](https://slideslive.com/38959212/debiasing-model-updates-for-improving-personalized-federated-training)] - One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. [[PUB](http://proceedings.mlr.press/v139/blum21a.html)] [[PDF](https://arxiv.org/abs/2103.03228)] [[CODE](https://github.com/rlphilli/Collaborative-Incentives)] [[VIDEO](https://slideslive.com/38959135/one-for-one-or-all-for-all-equilibria-and-optimality-of-collaboration-in-federated-learning)] - CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. [[PUB](http://proceedings.mlr.press/v139/xie21a.html)] [[PDF](https://arxiv.org/abs/2106.08283)] [[CODE](https://github.com/AI-secure/CRFL)] [[VIDEO](https://slideslive.com/38959047/crfl-certifiably-robust-federated-learning-against-backdoor-attacks)] - Federated Learning under Arbitrary Communication Patterns. [[PUB](http://proceedings.mlr.press/v139/avdiukhin21a.html)] [[VIDEO](https://slideslive.com/38959048/federated-learning-under-arbitrary-communication-patterns)] - Data-Free Knowledge Distillation for Heterogeneous Federated Learning. [[PUB](http://proceedings.mlr.press/v139/zhu21b.html)] - Personalized Federated Learning using Hypernetworks. [[PUB](http://proceedings.mlr.press/v139/shamsian21a.html)] - The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. [[PUB](http://proceedings.mlr.press/v139/kairouz21a.html)] - Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. [[PUB](http://proceedings.mlr.press/v139/data21a.html)] - Communication-Efficient Distributed Optimization with Quantized Preconditioners. [[PUB](http://proceedings.mlr.press/v139/alimisis21a.html)] - Communication-Efficient Distributed SVD via Local Power Iterations. [[PUB](http://proceedings.mlr.press/v139/li21u.html)] - Matrix Sketching for Secure Collaborative Machine Learning. [[PUB](http://proceedings.mlr.press/v139/zhang21v.html)] #### NeurIPS - CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. [[PUB](https://openreview.net/forum?id=eNB4WXnNczJ)] [[PDF](https://arxiv.org/abs/2107.09461)] - Boosting with Multiple Sources. [[PUB](https://openreview.net/forum?id=1oP1duoZxx)] - DRIVE: One-bit Distributed Mean Estimation. [[PUB](https://openreview.net/forum?id=KXRTmcv3dQ8)] [[CODE](https://github.com/amitport/DRIVE-One-bit-Distributed-Mean-Estimation)] - Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. [[PUB](https://openreview.net/forum?id=yRfsADObu18)] [[CODE](https://github.com/XinyiYS/Gradient-Driven-Rewards-to-Guarantee-Fairness-in-Collaborative-Machine-Learning)] - Gradient Inversion with Generative Image Prior. [[PUB](https://papers.nips.cc/paper/2021/hash/fa84632d742f2729dc32ce8cb5d49733-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.14962)] [[CODE](https://github.com/ml-postech/gradient-inversion-generative-image-prior)] - Distributed Machine Learning with Sparse Heterogeneous Data. [[PUB](https://openreview.net/forum?id=F9HNBbytcqT)] [[PDF](https://arxiv.org/abs/1912.01417)] - Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. [[PUB](https://openreview.net/forum?id=SPrVNsXnGd)] [[PDF](https://arxiv.org/abs/2107.08763)] - Sageflow: Robust Federated Learning against Both Stragglers and Adversaries. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/076a8133735eb5d7552dc195b125a454-Abstract.html)] - CAFE: Catastrophic Data Leakage in Vertical Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/08040837089cdf46631a10aca5258e16-Abstract.html)] [[CODE](https://github.com/DeRafael/CAFE)] - Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. [[PUB](https://papers.nips.cc/paper/2021/hash/080acdcce72c06873a773c4311c2e464-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.14074)] [[CODE](https://github.com/flint-xf-fan/Byzantine-Federeated-RL)] - Optimality and Stability in Federated Learning: A Game-theoretic Approach. [[PUB](https://papers.nips.cc/paper/2021/hash/09a5e2a11bea20817477e0b1dfe2cc21-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.09580)] [[CODE](https://github.com/kpdonahue/model_sharing_games)] - QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/1dba3025b159cd9354da65e2d0436a31-Abstract.html)] [[PDF](https://arxiv.org/abs/2107.13892)] [[CODE](https://github.com/zkhku/fedsage)] [[解读](https://zhuanlan.zhihu.com/p/430789355)] - The Skellam Mechanism for Differentially Private Federated Learning :fire:. [[PUB](https://papers.neurips.cc/paper/2021/hash/285baacbdf8fda1de94b19282acd23e2-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.04995)] [[CODE](https://github.com/google-research/federated/tree/master/distributed_dp)] - No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data. [[PUB](https://papers.neurips.cc/paper/2021/hash/2f2b265625d76a6704b08093c652fd79-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.05001)] - STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning. [[PUB](https://papers.neurips.cc/paper/2021/hash/3016a447172f3045b65f5fc83e04b554-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.10435)] - Subgraph Federated Learning with Missing Neighbor Generation. [[PUB](https://papers.neurips.cc/paper/2021/hash/34adeb8e3242824038aa65460a47c29e-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.13430)] [[CODE](https://github.com/zkhku/fedsage)] [[解读](https://zhuanlan.zhihu.com/p/423555171)] - Evaluating Gradient Inversion Attacks and Defenses in Federated Learning :fire:. [[PUB](https://papers.neurips.cc/paper/2021/hash/3b3fff6463464959dcd1b68d0320f781-Abstract.html)] [[PDF](https://arxiv.org/abs/2112.00059)] [[CODE](https://github.com/Princeton-SysML/GradAttack)] - Personalized Federated Learning With Gaussian Processes. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/46d0671dd4117ea366031f87f3aa0093-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.15482)] [[CODE](https://github.com/IdanAchituve/pFedGP)] - Differentially Private Federated Bayesian Optimization with Distributed Exploration. [[PUB](https://papers.nips.cc/paper/2021/hash/4c27cea8526af8cfee3be5e183ac9605-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.14153)] [[CODE](https://github.com/daizhongxiang/Differentially-Private-Federated-Bayesian-Optimization)] - Parameterized Knowledge Transfer for Personalized Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/5383c7318a3158b9bc261d0b6996f7c2-Abstract.html)] [[PDF](https://arxiv.org/abs/2111.02862)] [[CODE](https://github.com/cugzj/KT-pFL)] - Federated Reconstruction: Partially Local Federated Learning :fire:. [[PUB](https://papers.nips.cc/paper/2021/hash/5d44a2b0d85aa1a4dd3f218be6422c66-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.03448)] [[CODE](https://github.com/google-research/federated/tree/master/reconstruction)] [[UC.](https://github.com/KarhouTam/FedRecon)] - Fast Federated Learning in the Presence of Arbitrary Device Unavailability. [[PUB](https://papers.nips.cc/paper/2021/hash/64be20f6dd1dd46adf110cf871e3ed35-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.04159)] [[CODE](https://github.com/hmgxr128/MIFA_code/)] - FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective. [[PUB](https://papers.nips.cc/paper/2021/hash/692baebec3bb4b53d7ebc3b9fabac31b-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.13864)] [[CODE](https://github.com/jeremy313/FL-WBC)] - FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. [[PUB](https://papers.nips.cc/paper/2021/hash/6aed000af86a084f9cb0264161e29dd3-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.13451)] - Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients. [[PUB](https://papers.nips.cc/paper/2021/hash/7a6bda9ad6ffdac035c752743b7e9d0e-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.07053)] [[VIDEO](https://papertalk.org/papertalks/35898)] - Federated Multi-Task Learning under a Mixture of Distributions. [[PUB](https://papers.nips.cc/paper/2021/hash/82599a4ec94aca066873c99b4c741ed8-Abstract.html)] [[PDF](https://arxiv.org/abs/2108.10252)] [[CODE](https://github.com/omarfoq/FedEM)] - Federated Graph Classification over Non-IID Graphs. [[PUB](https://papers.nips.cc/paper/2021/hash/9c6947bd95ae487c81d4e19d3ed8cd6f-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.13423)] [[CODE](https://github.com/Oxfordblue7/GCFL)] [[解读](https://zhuanlan.zhihu.com/p/430718887)] - Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. [[PUB](https://papers.nips.cc/paper/2021/hash/a0205b87490c847182672e8d371e9948-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.04502)] [[CODE](https://github.com/mkhodak/fedex)] - On Large-Cohort Training for Federated Learning :fire:. [[PUB](https://papers.nips.cc/paper/2021/hash/ab9ebd57177b5106ad7879f0896685d4-Abstract.html)] [[PDF](https://arxiv.org/abs/2106.07820)] [[CODE](https://github.com/google-research/federated/tree/f4e26c1b9b47ac320e520a8b9943ea2c5324b8c2/large_cohort)] - DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/b0ab42fcb7133122b38521d13da7120b-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.03112)] [[CODE](https://github.com/hangxu0304/DeepReduce)] - PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization. [[PUB](https://papers.nips.cc/paper/2021/hash/c429429bf1f2af051f2021dc92a8ebea-Abstract.html)] [[VIDEO](https://papertalk.org/papertalks/37327)] - Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis. [[PUB](https://papers.nips.cc/paper/2021/hash/ceb0595112db2513b9325a85761b7310-Abstract.html)] [[PDF](https://arxiv.org/abs/2111.01338)] - Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/db8e1af0cb3aca1ae2d0018624204529-Abstract.html)] [[PDF](https://arxiv.org/abs/2108.08435)] [[CODE](https://github.com/cuis15/FCFL)] - Federated Linear Contextual Bandits. [[PUB](https://papers.nips.cc/paper/2021/hash/e347c51419ffb23ca3fd5050202f9c3d-Abstract.html)] [[PDF](https://arxiv.org/abs/2110.14177)] [[CODE](https://github.com/Ruiquan5514/Federated-Linear-Contextual-Bandits)] - Few-Round Learning for Federated Learning. [[PUB](https://papers.nips.cc/paper/2021/hash/f065d878ccfb4cc4f4265a4ff8bafa9a-Abstract.html)] - Breaking the centralized barrier for cross-device federated learning. [[PUB](https://papers.nips.cc/paper/2021/hash/f0e6be4ce76ccfa73c5a540d992d0756-Abstract.html)] [[CODE](https://fedjax.readthedocs.io/en/latest/fedjax.algorithms.html#module-fedjax.algorithms.mime)] [[VIDEO](https://papertalk.org/papertalks/37564)] - Federated-EM with heterogeneity mitigation and variance reduction. [[PUB](https://papers.nips.cc/paper/2021/hash/f740c8d9c193f16d8a07d3a8a751d13f-Abstract.html)] [[PDF](https://arxiv.org/abs/2111.02083)] - Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/fc03d48253286a798f5116ec00e99b2b-Abstract.html)] [[PAGE](https://dga.hanlab.ai/)] [[SLIDE](https://dga.hanlab.ai/assets/dga_slides.pdf)] - FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. [[PUB](https://papers.nips.cc/paper/2021/hash/fe7ee8fc1959cc7214fa21c4840dff0a-Abstract.html)] [[PDF](https://arxiv.org/abs/2103.03452)] [[CODE](https://github.com/unc-optimization/FedDR)] - Catastrophic Data Leakage in Vertical Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/08040837089cdf46631a10aca5258e16-Abstract.html)] - Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/3b3fff6463464959dcd1b68d0320f781-Abstract.html)] - Federated Reconstruction: Partially Local Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/5d44a2b0d85aa1a4dd3f218be6422c66-Abstract.html)] - On Large-Cohort Training for Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/ab9ebd57177b5106ad7879f0896685d4-Abstract.html)] - The Skellam Mechanism for Differentially Private Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/285baacbdf8fda1de94b19282acd23e2-Abstract.html)] - Asynchronous Decentralized SGD with Quantized and Local Updates. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/362c99307cdc3f2d8b410652386a9dd1-Abstract.html)] - CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/56577889b3c1cd083b6d7b32d32f99d5-Abstract.html)] - Communication-efficient SGD: From Local SGD to One-Shot Averaging. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/cc06a6150b92e17dd3076a0f0f9d2af4-Abstract.html)] - Distributed Deep Learning In Open Collaborations. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/41a60377ba920919939d83326ebee5a1-Abstract.html)] - Learning Collaborative Policies to Solve NP-hard Routing Problems. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/564127c03caab942e503ee6f810f54fd-Abstract.html)] - Learning Distilled Collaboration Graph for Multi-Agent Perception. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/f702defbc67edb455949f46babab0c18-Abstract.html)] - Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer. [[PUB](https://proceedings.neurips.cc/paper/2021/hash/5c53292c032b6cb8510041c54274e65f-Abstract.html)] - Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning). [[PUB](https://proceedings.neurips.cc/paper/2021/hash/d2cd33e9c0236a8c2d8bd3fa91ad3acf-Abstract.html)] ### 2020 #### ICLR - Federated Adversarial Domain Adaptation. [[PUB](https://openreview.net/forum?id=HJezF3VYPB)] [[PDF](https://arxiv.org/abs/1911.02054)] [[CODE](https://drive.google.com/file/d/1OekTpqB6qLfjlE2XUjQPm3F110KDMFc0/view?usp=sharing)] - DBA: Distributed Backdoor Attacks against Federated Learning. [[PUB](https://openreview.net/forum?id=rkgyS0VFvr)] [[CODE](https://github.com/AI-secure/DBA)] - Fair Resource Allocation in Federated Learning :fire:. [[PUB](https://openreview.net/forum?id=ByexElSYDr)] [[PDF](https://arxiv.org/abs/1905.10497)] [[CODE](https://github.com/litian96/fair_flearn)] - Federated Learning with Matched Averaging :fire:. [[PUB](https://openreview.net/forum?id=BkluqlSFDS)] [[PDF](https://arxiv.org/abs/2002.06440)] [[CODE](https://github.com/IBM/FedMA)] - Differentially Private Meta-Learning. [[PUB](https://openreview.net/forum?id=rJgqMRVYvr)] [[PDF](https://proceedings.mlr.press/v162/zhang22p.html)] - Generative Models for Effective ML on Private, Decentralized Datasets :fire:. [[PUB](https://openreview.net/forum?id=SJgaRA4FPH)] [[PDF](https://arxiv.org/abs/1911.06679)] [[CODE](https://github.com/google-research/federated/tree/master/gans)] - On the Convergence of FedAvg on Non-IID Data :fire:. [[PUB](https://openreview.net/forum?id=HJxNAnVtDS)] [[PDF](https://arxiv.org/abs/1907.02189)] [[CODE](https://github.com/lx10077/fedavgpy)] [[解读](https://zhuanlan.zhihu.com/p/500005337)] - Fair Resource Allocation in Federated Learning. [[PUB](https://openreview.net/forum?id=ByexElSYDr)] - Federated Learning with Matched Averaging. [[PUB](https://openreview.net/forum?id=BkluqlSFDS)] - Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication. [[PUB](https://openreview.net/forum?id=SJxZnR4YvB)] - Don't Use Large Mini-batches, Use Local SGD. [[PUB](https://openreview.net/forum?id=B1eyO1BFPr)] - On the Convergence of FedAvg on Non-IID Data. [[PUB](https://openreview.net/forum?id=HJxNAnVtDS)] - SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum. [[PUB](https://openreview.net/forum?id=SkxJ8REYPH)] #### ICML - FedBoost: A Communication-Efficient Algorithm for Federated Learning. [[PUB](http://proceedings.mlr.press/v119/hamer20a.html)] [[VIDEO](https://slideslive.com/38928463/fedboost-a-communicationefficient-algorithm-for-federated-learning?ref=speaker-16993-latest)] - FetchSGD: Communication-Efficient Federated Learning with Sketching. [[PUB](http://proceedings.mlr.press/v119/rothchild20a.html)] [[PDF](https://arxiv.org/abs/2007.07682)] [[VIDEO](https://slideslive.com/38928454/fetchsgd-communicationefficient-federated-learning-with-sketching)] [[CODE](https://github.com/kiddyboots216/CommEfficient)] - SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. [[PUB](http://proceedings.mlr.press/v119/karimireddy20a.html)] [[PDF](https://arxiv.org/abs/1910.06378)] [[VIDEO](https://slideslive.com/38927610/scaffold-stochastic-controlled-averaging-for-federated-learning)] [[UC.](https://github.com/ramshi236/Accelerated-Federated-Learning-Over-MAC-in-Heterogeneous-Networks)] [[解读](https://zhuanlan.zhihu.com/p/538941775)] - Federated Learning with Only Positive Labels. [[PUB](http://proceedings.mlr.press/v119/yu20f.html)] [[PDF](https://arxiv.org/abs/2004.10342)] [[VIDEO](https://slideslive.com/38928322/federated-learning-with-only-positive-labels)] - From Local SGD to Local Fixed-Point Methods for Federated Learning. [[PUB](http://proceedings.mlr.press/v119/malinovskiy20a.html)] [[PDF](https://arxiv.org/abs/2004.01442)] [[SLIDE](https://icml.cc/media/Slides/icml/2020/virtual)] [[VIDEO](https://slideslive.com/38928320/from-local-sgd-to-local-fixed-point-methods-for-federated-learning)] - Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. [[PUB](http://proceedings.mlr.press/v119/li20g.html)] [[PDF](https://arxiv.org/abs/2002.11364)] [[SLIDE](https://icml.cc/media/Slides/icml/2020/virtual)] [[VIDEO](https://slideslive.com/38927921/acceleration-for-compressed-gradient-descent-in-distributed-optimization)] - A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. [[PUB](http://proceedings.mlr.press/v119/koloskova20a.html)] - Collaborative Machine Learning with Incentive-Aware Model Rewards. [[PUB](http://proceedings.mlr.press/v119/sim20a.html)] - Communication-Efficient Distributed PCA by Riemannian Optimization. [[PUB](http://proceedings.mlr.press/v119/huang20e.html)] - Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. [[PUB](http://proceedings.mlr.press/v119/guo20f.html)] - Is Local SGD Better than Minibatch SGD?. [[PUB](http://proceedings.mlr.press/v119/woodworth20a.html)] - Manifold Identification for Ultimately Communication-Efficient Distributed Optimization. [[PUB](http://proceedings.mlr.press/v119/li20b.html)] #### jmlr - GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning. [[PUB](https://jmlr.org/papers/v21/19-718.html)] #### machine learning - Communication-efficient distributed multi-task learning with matrix sparsity regularization. [[PUB](https://doi.org/10.1007/s10994-019-05847-6)] #### NeurIPS - Differentially-Private Federated Linear Bandits. [[PUB](https://papers.nips.cc/paper/2020/hash/4311359ed4969e8401880e3c1836fbe1-Abstract.html)] [[PDF](https://arxiv.org/abs/2010.11425)] [[CODE](https://github.com/abhimanyudubey/private_federated_linear_bandits)] - Federated Principal Component Analysis. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/47a658229eb2368a99f1d032c8848542-Abstract.html)] [[PDF](https://arxiv.org/abs/1907.08059)] [[CODE](https://github.com/andylamp/federated_pca)] - FedSplit: an algorithmic framework for fast federated optimization. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/4ebd440d99504722d80de606ea8507da-Abstract.html)] [[PDF](https://arxiv.org/abs/2005.05238)] - Federated Bayesian Optimization via Thompson Sampling. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/6dfe08eda761bd321f8a9b239f6f4ec3-Abstract.html)] [[PDF](https://arxiv.org/abs/2010.10154)] [[CODE](https://github.com/daizhongxiang/Federated_Bayesian_Optimization)] - Lower Bounds and Optimal Algorithms for Personalized Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/187acf7982f3c169b3075132380986e4-Abstract.html)] [[PDF](https://arxiv.org/abs/2010.02372)] - Robust Federated Learning: The Case of Affine Distribution Shifts. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/f5e536083a438cec5b64a4954abc17f1-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.08907)] [[CODE](https://github.com/farzanfarnia/RobustFL)] - An Efficient Framework for Clustered Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/e32cc80bf07915058ce90722ee17bb71-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.04088)] [[CODE](https://github.com/jichan3751/ifca)] - Distributionally Robust Federated Averaging :fire:. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/ac450d10e166657ec8f93a1b65ca1b14-Abstract.html)] [[PDF](https://arxiv.org/abs/2102.12660)] [[CODE](https://github.com/MLOPTPSU/FedTorch)] - Personalized Federated Learning with Moreau Envelopes :fire:. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/f4f1f13c8289ac1b1ee0ff176b56fc60-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.08848)] [[CODE](https://github.com/CharlieDinh/pFedMe)] - Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/24389bfe4fe2eba8bf9aa9203a44cdad-Abstract.html)] [[PDF](https://arxiv.org/abs/2002.07948)] [[UC.](https://github.com/KarhouTam/Per-FedAvg)] - Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/a1d4c20b182ad7137ab3606f0e3fc8a4-Abstract.html)] [[PDF](https://arxiv.org/abs/2007.14513)] [[CODE](https://github.com/FedML-AI/FedML/tree/master/fedml_experiments/distributed/fedgkt)] [[解读](https://zhuanlan.zhihu.com/p/536901871)] - Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization :fire:. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/564127c03caab942e503ee6f810f54fd-Abstract.html)] [[PDF](https://arxiv.org/abs/2007.07481)] [[CODE](https://github.com/JYWa/FedNova)] [[UC.](https://github.com/carbonati/fl-zoo)] - Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/b8ffa41d4e492f0fad2f13e29e1762eb-Abstract.html)] [[PDF](https://arxiv.org/abs/2007.05084)] - Federated Accelerated Stochastic Gradient Descent. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/39d0a8908fbe6c18039ea8227f827023-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.08950)] [[CODE](https://github.com/hongliny/FedAc-NeurIPS20)] [[VIDEO](https://youtu.be/K28zpAgg3HM)] - Inverting Gradients - How easy is it to break privacy in federated learning? :fire:. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/c4ede56bbd98819ae6112b20ac6bf145-Abstract.html)] [[PDF](https://arxiv.org/abs/2003.14053)] [[CODE](https://github.com/JonasGeiping/invertinggradients)] - Ensemble Distillation for Robust Model Fusion in Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/18df51b97ccd68128e994804f3eccc87-Abstract.html)] [[PDF](https://arxiv.org/abs/2006.07242)] [[CODE](https://github.com/epfml/federated-learning-public-code/tree/master/codes/FedDF-code)] - Throughput-Optimal Topology Design for Cross-Silo Federated Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/e29b722e35040b88678e25a1ec032a21-Abstract.html)] [[PDF](https://arxiv.org/abs/2010.12229)] [[CODE](https://github.com/omarfoq/communication-in-cross-silo-fl)] - Distributionally Robust Federated Averaging. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/ac450d10e166657ec8f93a1b65ca1b14-Abstract.html)] - Inverting Gradients - How easy is it to break privacy in federated learning?. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/c4ede56bbd98819ae6112b20ac6bf145-Abstract.html)] - Personalized Federated Learning with Moreau Envelopes. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/f4f1f13c8289ac1b1ee0ff176b56fc60-Abstract.html)] - Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/564127c03caab942e503ee6f810f54fd-Abstract.html)] - A Scalable Approach for Privacy-Preserving Collaborative Machine Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/5bf8aaef51c6e0d363cbe554acaf3f20-Abstract.html)] - Minibatch vs Local SGD for Heterogeneous Distributed Learning. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/45713f6ff2041d3fdfae927b82488db8-Abstract.html)] - ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. [[PUB](https://proceedings.neurips.cc/paper/2020/hash/9d58963592071dbf38a0fa114269959c-Abstract.html)] ### 2019 #### colt - Communication and Memory Efficient Testing of Discrete Distributions. [[PUB](http://proceedings.mlr.press/v99/diakonikolas19a.html)] #### iclr - Local SGD Converges Fast and Communicates Little. [[PUB](https://openreview.net/forum?id=S1g2JnRcFX)] #### ICML - Bayesian Nonparametric Federated Learning of Neural Networks :fire:. [[PUB](http://proceedings.mlr.press/v97/yurochkin19a.html)] [[PDF](https://arxiv.org/abs/1905.12022)] [[CODE](https://github.com/IBM/probabilistic-federated-neural-matching)] - Analyzing Federated Learning through an Adversarial Lens :fire:. [[PUB](http://proceedings.mlr.press/v97/bhagoji19a.html)] [[PDF](https://arxiv.org/abs/1811.12470)] [[CODE](https://github.com/inspire-group/ModelPoisoning)] - Agnostic Federated Learning. [[PUB](http://proceedings.mlr.press/v97/mohri19a.html)] [[PDF](https://arxiv.org/abs/1902.00146)] - Analyzing Federated Learning through an Adversarial Lens. [[PUB](http://proceedings.mlr.press/v97/bhagoji19a.html)] - Bayesian Nonparametric Federated Learning of Neural Networks. [[PUB](http://proceedings.mlr.press/v97/yurochkin19a.html)] - Collaborative Evolutionary Reinforcement Learning. [[PUB](http://proceedings.mlr.press/v97/khadka19a.html)] - Learning to Collaborate in Markov Decision Processes. [[PUB](http://proceedings.mlr.press/v97/radanovic19a.html)] - On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization. [[PUB](http://proceedings.mlr.press/v97/yu19d.html)] #### jmlr - Deep Reinforcement Learning for Swarm Systems. [[PUB](https://jmlr.org/papers/v20/18-476.html)] #### machine learning - Collaborative topic regression for predicting topic-based social influence. [[PUB](https://doi.org/10.1007/s10994-018-05776-w)] #### neurips - Communication trade-offs for Local-SGD with large step size. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/4aadd661908b181d059a117f02fbc9ec-Abstract.html)] - Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/80c0e8c4457441901351e4abbcf8c75c-Abstract.html)] - Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/4e87337f366f72daa424dae11df0538c-Abstract.html)] - Communication-efficient Distributed SGD with Sketching. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/75da5036f659fe64b53f3d9b39412967-Abstract.html)] - Double Quantization for Communication-Efficient Distributed Optimization. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/ea4eb49329550caaa1d2044105223721-Abstract.html)] - Learning to Optimize in Swarms. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/ec04e8ebba7e132043e5b4832e54f070-Abstract.html)] - Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/c17028c9b6e0c5deaad29665d582284a-Abstract.html)] - Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/d202ed5bcfa858c15a9f383c3e386ab2-Abstract.html)] - Robust and Communication-Efficient Collaborative Learning. [[PUB](https://proceedings.neurips.cc/paper/2019/hash/3eb2f1a06667bfb9daba7f7effa0284b-Abstract.html)] ### 2018 #### icml - An Alternative View: When Does SGD Escape Local Minima?. [[PUB](http://proceedings.mlr.press/v80/kleinberg18a.html)] #### NeurIPS - cpSGD: Communication-efficient and differentially-private distributed SGD. [[PUB](https://papers.nips.cc/paper/2018/hash/21ce689121e39821d07d04faab328370-Abstract.html)] [[PDF](https://arxiv.org/abs/1805.10559)] - Collaborative Learning for Deep Neural Networks. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/430c3626b879b4005d41b8a46172e0c0-Abstract.html)] - Gradient Sparsification for Communication-Efficient Distributed Optimization. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/3328bdf9a4b9504b9398284244fe97c2-Abstract.html)] - Improved Algorithms for Collaborative PAC Learning. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/3569df159ec477451530c4455b2a9e86-Abstract.html)] - LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/feecee9f1643651799ede2740927317a-Abstract.html)] - Tight Bounds for Collaborative PAC Learning via Multiplicative Weights. [[PUB](https://proceedings.neurips.cc/paper/2018/hash/ed519dacc89b2bead3f453b0b05a4a8b-Abstract.html)] #### uai - Probabilistic Collaborative Representation Learning for Personalized Item Recommendation. [[PUB](http://auai.org/uai2018/proceedings/papers/354.pdf)] ### 2017 #### colt - Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox. [[PUB](http://proceedings.mlr.press/v65/wang17a.html)] #### icml - Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. [[PUB](http://proceedings.mlr.press/v70/garber17a.html)] #### jmlr - CoCoA: A General Framework for Communication-Efficient Distributed Optimization. [[PUB](https://jmlr.org/papers/v18/16-512.html)] - Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models. [[PUB](https://jmlr.org/papers/v18/15-397.html)] #### machine learning - Collaborative topic regression for online recommender systems: an online and Bayesian approach. [[PUB](https://doi.org/10.1007/s10994-016-5599-z)] #### NeurIPS - Federated Multi-Task Learning :fire:. [[PUB](https://papers.nips.cc/paper/2017/hash/6211080fa89981f66b1a0c9d55c61d0f-Abstract.html)] [[PDF](https://arxiv.org/abs/1705.10467)] [[CODE](https://github.com/gingsmith/fmtl)] #### uai - Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem. [[PUB](http://auai.org/uai2017/proceedings/papers/286.pdf)] ### 2016 #### jmlr - The Statistical Performance of Collaborative Inference. [[PUB](https://jmlr.org/papers/v17/15-346.html)] ### 2015 #### machine learning - Random drift particle swarm optimization algorithm: convergence analysis and parameter selection. [[PUB](https://doi.org/10.1007/s10994-015-5522-z)] ### 2014 #### icml - Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. [[PUB](http://proceedings.mlr.press/v32/shamir14.html)] #### machine learning - Collaborative filtering with information-rich and information-sparse entities. [[PUB](https://doi.org/10.1007/s10994-014-5454-z)] - Collaborative information acquisition for data-driven decisions. [[PUB](https://doi.org/10.1007/s10994-013-5424-x)] - Detecting inappropriate access to electronic health records using collaborative filtering. [[PUB](https://doi.org/10.1007/s10994-013-5376-1)] ### 2012 #### jmlr - SVDFeature: a toolkit for feature-based collaborative filtering. [[PUB](https://dl.acm.org/doi/10.5555/2503308.2503357)] ### 2011 #### colt - Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing. [[PUB](http://proceedings.mlr.press/v19/shamir11a/shamir11a.pdf)] #### machine learning - Editorial survey: swarm intelligence for data mining. [[PUB](https://doi.org/10.1007/s10994-010-5216-5)] - Particle swarm optimizer for variable weighting in clustering high-dimensional data. [[PUB](https://doi.org/10.1007/s10994-009-5154-2)] ### 2009 #### icml - Transfer learning for collaborative filtering via a rating-matrix generative model. [[PUB](https://doi.org/10.1145/1553374.1553454)] #### jmlr - A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. [[PUB](https://dl.acm.org/doi/10.5555/1577069.1577098)] - Particle Swarm Model Selection. [[PUB](https://dl.acm.org/doi/10.5555/1577069.1577084)] - Scalable Collaborative Filtering Approaches for Large Recommender Systems. [[PUB](https://dl.acm.org/doi/10.5555/1577069.1577091)] ### 2008 #### machine learning - A collaborative filtering framework based on both local user similarity and global user similarity. [[PUB](https://doi.org/10.1007/s10994-008-5068-4)] ### 2006 #### jmlr - Collaborative Multiagent Reinforcement Learning by Payoff Propagation. [[PUB](https://jmlr.org/papers/v7/kok06a.html)] ### 2005 #### colt - Competitive Collaborative Learning. [[PUB](https://doi.org/10.1007/11503415_16)] ### 2004 #### machine learning - Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. [[PUB](https://doi.org/10.1023/B:MACH.0000035516.74817.51)] #### uai - A Bayesian Approach toward Active Learning for Collaborative Filtering. [[PUB](https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=1119&proceeding_id=20)] ### 2003 #### machine learning - A Theoretical Analysis of Query Selection for Collaborative Filtering. [[PUB](https://doi.org/10.1023/A:1022961719072)] #### uai - Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes. [[PUB](https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=981&proceeding_id=19)] ### 2000 #### jmlr - Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. [[PUB](https://jmlr.org/papers/v1/heckerman00a.html)] #### tpami - Merging and Splitting Eigenspace Models. [[PUB](https://doi.org/10.1109/34.877525)]

## fl in top dm conference and journal Federated Learning papers accepted by top DM(Data Mining) conference and journal, Including [KDD](https://dblp.uni-trier.de/db/conf/kdd/index.html)(ACM SIGKDD Conference on Knowledge Discovery and Data Mining) and [WSDM](https://dblp.uni-trier.de/db/conf/wsdm/index.html)(Web Search and Data Mining). - [KDD](https://dblp.uni-trier.de/search?q=federate%20venue%3AKDD%3A) [2026](https://dl.acm.org/doi/proceedings/10.1145/3770854), [2025](https://dl.acm.org/doi/proceedings/10.1145/3690624), [2024](https://dl.acm.org/doi/proceedings/10.1145/3637528), [2023](https://dl.acm.org/doi/proceedings/10.1145/3580305)([Research Track](https://kdd.org/kdd2023/research-track-papers/), [Applied Data Science track](https://kdd.org/kdd2023/ads-track-papers/), [Workshop](https://fl4data-mining.github.io/papers/)), 2022([Research Track](https://kdd.org/kdd2022/paperRT.html), [Applied Data Science track](https://kdd.org/kdd2022/paperADS.html)), [2021](https://kdd.org/kdd2021/accepted-papers/index), [2020](https://www.kdd.org/kdd2020/accepted-papers) - [WSDM](https://dblp.uni-trier.de/search?q=federate%20venue%3AWSDM%3A) [2026](https://dl.acm.org/doi/proceedings/10.1145/3773966), [2025](https://www.wsdm-conference.org/2025/accepted-papers/), [2024](https://www.wsdm-conference.org/2024/accepted-papers/), [2023](https://www.wsdm-conference.org/2023/program/accepted-papers), [2022](https://www.wsdm-conference.org/2022/accepted-papers/), [2021](https://www.wsdm-conference.org/2021/accepted-papers.php), [2019](https://www.wsdm-conference.org/2019/accepted-papers.php)

fl in top dm conference and journal

### 2026 #### KDD - Caesar: Optimizing Federated Learning via Low-deviation Compression. [[PUB](https://doi.org/10.1145/3770854.3780170)] - Communication-efficient Federated Graph Classification via Generative Diffusion Modeling. [[PUB](https://doi.org/10.1145/3770854.3780262)] - FedKDMR: Robust Federated Learning via Joint Knowledge Distillation & Model Recombination. [[PUB](https://doi.org/10.1145/3770854.3780160)] - FedPRE: Robust Federated Graph Learning against Topological Corruption. [[PUB](https://doi.org/10.1145/3770854.3780330)] - HAL: Accurate, Private, and Efficient Sample Alignment for Multimodal Federated Learning. [[PUB](https://doi.org/10.1145/3770854.3780223)] - MFC: Mixed Federated Clustering based on Cross-modal Feature Decoupling. [[PUB](https://doi.org/10.1145/3770854.3780327)] - Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking. [[PUB](https://doi.org/10.1145/3770854.3780255)] - Two Heads Are Better Than One: Generalized Cross-Domain Federated Learning via Dual-Prototype. [[PUB](https://doi.org/10.1145/3770854.3780269)] - Vertical Federated K-Means for Multi-View Data Guided by a K-Means Cost Bound after Projection. [[PUB](https://doi.org/10.1145/3770854.3780182)] - MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation. [[PUB](https://doi.org/10.1145/3770854.3780264)] #### WSDM - Federated Watermarking of Deep Neural Networks with Distributed Verification. [[PUB](https://doi.org/10.1145/3773966.3777930)] - Sharpness-aware Federated Graph Learning. [[PUB](https://doi.org/10.1145/3773966.3777989)] ### 2025 #### KDD - A Unified Solution to Diverse Heterogeneities in One-Shot Federated Learning. [[PUB](https://doi.org/10.1145/3711896.3736825)] [[CODE](https://github.com/Jun-B0518/FedHydra)] - Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis. [[PUB](https://doi.org/10.1145/3690624.3709235)] - Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training. [[PUB](https://doi.org/10.1145/3690624.3709284)] - BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning. [[PUB](https://doi.org/10.1145/3690624.3709309)] [[CODE](https://github.com/ZhouYuCS/BTFL)] - DarkDistill: Difficulty-Aligned Federated Early-Exit Network Training on Heterogeneous Devices. [[PUB](https://doi.org/10.1145/3711896.3736902)] - FedAPM: Federated Learning via ADMM with Partial Model Personalization. [[PUB](https://doi.org/10.1145/3711896.3736954)] - FedDiAL: Adaptive Federated Learning with Hierarchical Discriminative Network for Large Pre-trained Models. [[PUB](https://doi.org/10.1145/3711896.3736955)] - Federated Continual Graph Learning. [[PUB](https://doi.org/10.1145/3711896.3736956)] - FedGuCci: Making Local Models More Connected in Landscape for Federated Learning. [[PUB](https://doi.org/10.1145/3711896.3737037)] [[CODE](https://github.com/ZexiLee/fedgucci)] - FedKDD 2025: The 2025 International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. [[PUB](https://doi.org/10.1145/3711896.3737861)] - FedMetro: Efficient Metro Passenger Flow Prediction via Federated Graph Learning. [[PUB](https://doi.org/10.1145/3711896.3737218)] [[CODE](https://github.com/AlexMufeng/FedMetro)] - FedSC: Federated Learning with Semantic-Aware Collaboration. [[PUB](https://doi.org/10.1145/3711896.3736957)] - FedVS: Towards Federated Vector Similarity Search with Filters. [[PUB](https://doi.org/10.1145/3711896.3736958)] - FEZE: Alignment-Flexible Zero-Shot Vertical Federated Learning. [[PUB](https://doi.org/10.1145/3711896.3736959)] - FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning. [[PUB](https://doi.org/10.1145/3690624.3709346)] - Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning. [[PUB](https://doi.org/10.1145/3690624.3709311)] - Gradients as An Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing. [[PUB](https://doi.org/10.1145/3711896.3736987)] [[CODE](https://github.com/mastlab-T3S/FedRAS)] - GuardFGL: Similarity-driven Federated Graph Learning with Adversarial Robustness and Membership Privacy. [[PUB](https://doi.org/10.1145/3711896.3736994)] - HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark. [[PUB](https://doi.org/10.1145/3711896.3737379)] [[CODE](https://github.com/TsingZ0/HtFLlib)] - PARSIFAL: Private and Robust Sign Federated Learning. [[PUB](https://doi.org/10.1145/3711896.3737074)] - PraFFL: A Preference-Aware Scheme in Fair Federated Learning. [[PUB](https://doi.org/10.1145/3690624.3709217)] [[CODE](https://github.com/rG223/PraFFL)] - Proxy-Validated Importance-Aware Federated Sample Selection with Meta Learning. [[PUB](https://doi.org/10.1145/3711896.3737093)] [[CODE](https://github.com/nameyzhang/FedSelect)] - Runtime-Aware Pipeline for Vertical Federated Learning with Bounded Model Staleness. [[PUB](https://doi.org/10.1145/3690624.3709243)] - Tackling Federated Long-Tailed Learning via Synthetic Feature-Based Decoupled Training. [[PUB](https://doi.org/10.1145/3711896.3737143)] - Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression. [[PUB](https://doi.org/10.1145/3690624.3709341)] [[CODE](https://github.com/JunliangLv/task_diversity_BFL)] - Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift. [[PUB](https://doi.org/10.1145/3711896.3737161)] - Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning. [[PUB](https://doi.org/10.1145/3711896.3737198)] - Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search. [[PUB](https://doi.org/10.1145/3690624.3709408)] #### WSDM - Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3703513)] - FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3703493)] - Towards Personalized Federated Multi-Scenario Multi-Task Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3703523)] - Density-aware and Cluster-based Federated Anomaly Detection on Data Streams. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3703548)] - Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research Data Management. [[PUB](https://dl.acm.org/doi/10.1145/3701551.3704125)] ### 2024 #### KDD - Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671722)] - *BadSampler:* Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671879)] - Federated Graph Learning with Structure Proxy Alignment. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671717)] [[CODE](https://github.com/xbfu/FedSpray)] - HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671660)] - FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671545)] - Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671590)] [[CODE](https://github.com/illidanlab/distributed-cluster-harmonization)] - FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671573)] [[CODE](https://github.com/alibaba/FederatedScope/tree/llm)] - On the Convergence of Zeroth-Order Federated Tuning for Large Language Models. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671865)] - CASA: Clustered Federated Learning with Asynchronous Clients. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671979)] - FLAIM: AIM-based Synthetic Data Generation in the Federated Setting. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671990)] - Privacy-Preserving Federated Learning using Flower Framework. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671447)] - FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671748)] [[CODE](https://github.com/wangzihuixmu/FedSAC)] - FedNLR: Federated Learning with Neuron-wise Learning Rates. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3672042)] - FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671897)] - FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671899)] [[CODE](https://github.com/XTxiatong/FLea.git)] - Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671663)] - PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671753)] - FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671906)] - FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671613)] [[CODE](https://github.com/LarryHawkingYoung/KDD2024_FedGTP)] - OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671582)] [[CODE](https://github.com/rui-ye/OpenFedLLM)] - Personalized Federated Continual Learning via Multi-Granularity Prompt. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671948)] - Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671908)] [[CODE](https://github.com/ZhangJiayuan-BUAA/FedTSA)] - GPFedRec: Graph-Guided Personalization for Federated Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671702)] [[CODE](https://github.com/Zhangcx19/GPFedRec)] - Asynchronous Vertical Federated Learning for Kernelized AUC Maximization. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671930)] - VertiMRF: Differentially Private Vertical Federated Data Synthesis. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671771)] - FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. [[PUB](https://dl.acm.org/doi/10.1145/3637528.3671490)] - Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations. [[PUB](https://doi.org/10.1145/3637528.3671743)] - High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. [[PUB](https://doi.org/10.1145/3637528.3672038)] [[CODE](https://github.com/FutureComputing4AI/ProxCSL)] - Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering. [[PUB](https://doi.org/10.1145/3637528.3671840)] [[CODE](https://github.com/wu1hong/SCCF)] #### WSDM - User Consented Federated Recommender System Against Personalized Attribute Inference Attack. [[PUB](https://dl.acm.org/doi/10.1145/3616855.3635830)] [[PDF](https://arxiv.org/abs/2312.16203)] [[CODE](https://github.com/hkust-knowcomp/uc-fedrec)] - Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3616855.3635758)] ### 2023 #### KDD - Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599475)] [[PDF](https://arxiv.org/abs/2210.11050)] - FedDefender: Client-Side Attack-Tolerant Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599346)] [[PDF](https://arxiv.org/abs/2307.09048)] [[CODE](https://github.com/deu30303/feddefender)] - FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599344)] [[CODE](https://github.com/zhenqincn/FedAPEN)] - FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599348)] [[PDF](https://arxiv.org/abs/2207.05247)] - ShapleyFL: Robust Federated Learning Based on Shapley Value. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599500)] [[CODE](https://github.com/ZJU-DIVER/ShapleyFL-Robust-Federated-Learning-Based-on-Shapley-Value)] - Federated Few-shot Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599347)] [[PDF](https://arxiv.org/abs/2306.10234)] [[CODE](https://github.com/songw-sw/f2l)] - Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599521)] - Personalized Federated Learning with Parameter Propagation. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599464)] - Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599499)] [[PDF](https://arxiv.org/abs/2308.03035)] [[CODE](https://github.com/xidongwu/D-AUPRC)] - CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599293)] [[PDF](https://arxiv.org/abs/2109.05613)] - FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599354)] [[PDF](https://arxiv.org/abs/2306.03834)] - FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599345)] [[PDF](https://arxiv.org/abs/2307.01217)] [[CODE](https://github.com/tsingz0/fedcp)] - Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599443)] [[PDF](https://arxiv.org/abs/2301.00489)] [[CODE](https://github.com/jiayunz/fedalign)] - DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599311)] [[CODE](https://github.com/garyzhang99/DM-PFL)] - FS-REAL: Towards Real-World Cross-Device Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599829)] [[PDF](https://arxiv.org/abs/2303.13363)] - FedMultimodal: A Benchmark for Multimodal Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599825)] [[PDF](https://arxiv.org/abs/2306.09486)] [[CODE](https://github.com/usc-sail/fed-multimodal)] - PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599889)] [[PDF](https://arxiv.org/abs/2204.08146)] [[NEWS](http://info.ruc.edu.cn/xwgg/xyxw/e4c838332c3a46cd8b959be49c021bb1.htm)] - Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599898)] [[PDF](https://arxiv.org/abs/2302.01677)] [[CODE](https://github.com/alibaba/FederatedScope/tree/backdoor-bench)] - UA-FedRec: Untargeted Attack on Federated News Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599923)] [[PDF](https://arxiv.org/abs/2202.06701)] [[CODE](https://github.com/yjw1029/ua-fedrec)] - International Workshop on Federated Learning for Distributed Data Mining. [[PUB](https://dl.acm.org/doi/10.1145/3580305.3599198)] [[PAGE](https://fl4data-mining.github.io/)] - Is Normalization Indispensable for Multi-domain Federated Learning?. [[PUB](https://openreview.net/forum?id=ZiaOEg8XiGN)] - Distributed Personalized Empirical Risk Minimization. [[PUB](https://openreview.net/forum?id=k2eYX1p-Yb)] - Once-for-All Federated Learning: Learning From and Deploying to Heterogeneous Clients. [[PUB](https://openreview.net/forum?id=aJhe-VC0Ue)] - SparseVFL: Communication-Efficient Vertical Federated Learning Based on Sparsification of Embeddings and Gradients. [[PUB](https://openreview.net/forum?id=BVH3-XCRoN3)] - Optimization of User Resources in Federated Learning for Urban Sensing Applications. [[PUB](https://openreview.net/forum?id=D6ZQJ-szypI)] - FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning. [[PUB](https://openreview.net/forum?id=nXjyCmLOYj)] - Federated Graph Analytics with Differential Privacy. [[PUB](https://openreview.net/forum?id=yBMbtNM3GR4)] - Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. [[PUB](https://openreview.net/forum?id=rAHB4qkWYz)] - Uncertainty Quantification in Federated Learning for Heterogeneous Health Data. [[PUB](https://openreview.net/forum?id=QSQOTUVQR46)] - A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. [[PUB](https://openreview.net/forum?id=pLEQFXACNA)] - Taming Heterogeneity to Deal with Test-Time Shift in Federated Learning. [[PUB](https://openreview.net/forum?id=_Nsxwk3WWew)] - Federated Blood Supply Chain Demand Forecasting: A Case Study. [[PUB](https://openreview.net/forum?id=2c0hdQDvf5g)] - Stochastic Clustered Federated Learning. [[PUB](https://openreview.net/forum?id=pFvTwedsUh)] - A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection. [[PUB](https://openreview.net/forum?id=jg3XzuNbS-0)] - Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis. [[PUB](https://openreview.net/forum?id=W7LqmnU4TYZ)] - FedNoisy: A Federated Noisy Label Learning Benchmark. [[PUB](https://openreview.net/forum?id=cXMenagKy-7)] - Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. [[PUB](https://openreview.net/forum?id=DZvNrRNas6z)] - Federated learning for competing risk analysis in healthcare. [[PUB](https://openreview.net/forum?id=-HYSYe7uXRT)] - Federated Threat Detection for Smart Home IoT rules. [[PUB](https://openreview.net/forum?id=SK_KfAh8MtF)] - A Collaborative Transfer Learning Framework for Cross-domain Recommendation. [[PUB](https://doi.org/10.1145/3580305.3599758)] - Communication Efficient and Differentially Private Logistic Regression under the Distributed Setting. [[PUB](https://doi.org/10.1145/3580305.3599279)] - Communication Efficient Distributed Newton Method with Fast Convergence Rates. [[PUB](https://doi.org/10.1145/3580305.3599280)] #### WSDM - Federated Unlearning for On-Device Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3539597.3570463)] [[PDF](https://arxiv.org/abs/2210.10958)] - 4th Crowd Science Workshop - CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling. [[PUB](https://doi.org/10.1145/3539597.3572703)] ### 2022 #### KDD - FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning :fire:. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539112)] [[PDF](https://arxiv.org/abs/2204.05562)] [[CODE](https://github.com/alibaba/FederatedScope)] - Collaboration Equilibrium in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539237)] [[PDF](https://arxiv.org/abs/2108.07926)] [[CODE](https://github.com/cuis15/learning-to-collaborate)] - Connected Low-Loss Subspace Learning for a Personalization in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539254)] [[PDF](https://arxiv.org/abs/2109.07628)] [[CODE](https://github.com/vaseline555/superfed)] - FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539384)] - Communication-Efficient Robust Federated Learning with Noisy Labels. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539328)] [[PDF](https://arxiv.org/abs/2206.05558)] - FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539231)] [[PDF](https://arxiv.org/abs/2207.09209)] [[CODE](https://github.com/zaixizhang/FLDetector)] - Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539402)] [[PDF](https://arxiv.org/abs/2105.08925)] [[CODE](https://github.com/Di-Chai/FedEval)] - FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539308)] [[PDF](https://arxiv.org/abs/2205.15896)] - Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539047)] [[PDF](https://hufudb.com/static/paper/2022/SIGKDD2022_Fed-LTD%20Towards%20Cross-Platform%20Ride%20Hailing%20via.pdf)] [[解读](https://zhuanlan.zhihu.com/p/544183874)] - Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539039)] [[PDF](https://arxiv.org/abs/2202.08036)] - No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539086)] [[PDF](https://arxiv.org/abs/2202.08036)] - FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling. [[PUB](https://dl.acm.org/doi/10.1145/3534678.3539119)] [[PDF](https://arxiv.org/abs/2202.04975)] [[CODE](https://github.com/wuch15/FedAttack)] - A Practical Introduction to Federated Learning. [[PUB](https://doi.org/10.1145/3534678.3542631)] - Connecting Low-Loss Subspace for Personalized Federated Learning. [[PUB](https://doi.org/10.1145/3534678.3539254)] - FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning. [[PUB](https://doi.org/10.1145/3534678.3539112)] [[CODE](https://github.com/alibaba/FederatedScope)] - FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients. [[PUB](https://doi.org/10.1145/3534678.3539231)] - Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning. [[PUB](https://doi.org/10.1145/3534678.3539022)] #### WSDM - PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion. [[PUB](https://dl.acm.org/doi/10.1145/3488560.3498386)] [[PDF](https://arxiv.org/abs/2110.10926)] - Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning. [[PUB](https://doi.org/10.1145/3488560.3498381)] ### 2021 #### KDD - Fed2: Feature-Aligned Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467309)] [[PDF](https://arxiv.org/abs/2111.14248)] - FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467254)] [[CODE](https://github.com/lxcnju/FedRepo)] - Federated Adversarial Debiasing for Fair and Trasnferable Representations. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467281)] [[PAGE](https://jyhong.gitlab.io/publication/fade2021kdd/)] [[CODE](https://github.com/illidanlab/FADE)] [[SLIDE](https://jyhong.gitlab.io/publication/fade2021kdd/slides.pdf)] - Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. [[PUB](https://dl.acm.org/doi/pdf/10.1145/3447548.3467371)] [[CODE](https://github.com/mengcz13/KDD2021_CNFGNN)] [[解读](https://zhuanlan.zhihu.com/p/434839878)] - AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467169)] [[PDF](https://arxiv.org/abs/2109.12519)] - FLOP: Federated Learning on Medical Datasets using Partial Networks. [[PUB](https://dl.acm.org/doi/10.1145/3447548.3467185)] [[PDF](https://arxiv.org/abs/2102.05218.pdf)] [[CODE](https://github.com/jianyizhang123/FLOP)] - Federated Adversarial Debiasing for Fair and Transferable Representations. [[PUB](https://doi.org/10.1145/3447548.3467281)] [[CODE](https://github.com/illidanlab/FADE)] - Towards Fair Federated Learning. [[PUB](https://doi.org/10.1145/3447548.3470814)] - Device-Cloud Collaborative Learning for Recommendation. [[PUB](https://doi.org/10.1145/3447548.3467097)] #### WSDM - A Practical Federated Learning Framework for Small Number of Stakeholders. [[PUB](https://dl.acm.org/doi/10.1145/3437963.3441702)] [[CODE](https://github.com/MTC-ETH/Federated-Learning-source)] - Federated Deep Knowledge Tracing. [[PUB](https://dl.acm.org/doi/10.1145/3437963.3441747)] [[CODE](https://github.com/hxwujinze/federated-deep-knowledge-tracing)] ### 2020 #### KDD - FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems. [[PUB](https://dl.acm.org/doi/10.1145/3394486.3403176)] [[VIDEO](https://papertalk.org/papertalks/23422)] - Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data. [[PUB](https://dl.acm.org/doi/10.1145/3394486.3403298)] [[PDF](https://arxiv.org/abs/2008.06197)] [[VIDEO](https://papertalk.org/papertalks/23301)] ### 2019 #### kdd - A Collaborative Learning Framework to Tag Refinement for Points of Interest. [[PUB](https://doi.org/10.1145/3292500.3330698)] - FDML: A Collaborative Machine Learning Framework for Distributed Features. [[PUB](https://doi.org/10.1145/3292500.3330765)] #### WSDM - Federated Online Learning to Rank with Evolution Strategies. [[PUB](https://dl.acm.org/doi/10.1145/3289600.3290968)] [[CODE](http://github.com/facebookresearch/foltr-es)] ### 2018 #### kdd - Collaborative Deep Metric Learning for Video Understanding. [[PUB](https://doi.org/10.1145/3219819.3219856)] - Multi-label Learning with Highly Incomplete Data via Collaborative Embedding. [[PUB](https://doi.org/10.1145/3219819.3220038)] #### wsdm - Robust Transfer Learning for Cross-domain Collaborative Filtering Using Multiple Rating Patterns Approximation. [[PUB](https://doi.org/10.1145/3159652.3159675)] ### 2017 #### kdd - Federated Tensor Factorization for Computational Phenotyping. [[PUB](https://doi.org/10.1145/3097983.3098118)] - Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation. [[PUB](https://doi.org/10.1145/3097983.3098094)] - Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines. [[PUB](https://doi.org/10.1145/3097983.3098080)] #### wsdm - Representation Learning with Pair-wise Constraints for Collaborative Ranking. [[PUB](https://doi.org/10.1145/3018661.3018720)] ### 2016 #### kdd - Communication Efficient Distributed Kernel Principal Component Analysis. [[PUB](https://doi.org/10.1145/2939672.2939796)] ### 2015 #### kdd - Collaborative Deep Learning for Recommender Systems. [[PUB](https://doi.org/10.1145/2783258.2783273)] ### 2014 #### kdd - Active collaborative permutation learning. [[PUB](https://doi.org/10.1145/2623330.2623730)] ### 2012 #### kdd - Learning binary codes for collaborative filtering. [[PUB](https://doi.org/10.1145/2339530.2339611)] #### wsdm - Beyond ten blue links: enabling user click modeling in federated web search. [[PUB](https://doi.org/10.1145/2124295.2124351)] ### 2011 #### wsdm - On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals. [[PUB](https://doi.org/10.1145/1935826.1935922)]

## fl in top secure conference and journal Federated Learning papers accepted by top Secure conference and journal, Including [S&P](https://dblp.uni-trier.de/db/conf/sp/index.html)(IEEE Symposium on Security and Privacy), [CCS](https://dblp.uni-trier.de/db/conf/ccs/index.html)(Conference on Computer and Communications Security), [USENIX Security](https://dblp.uni-trier.de/db/conf/uss/index.html)(Usenix Security Symposium) and [NDSS](https://dblp.uni-trier.de/db/conf/ndss/index.html)(Network and Distributed System Security Symposium). - [S&P](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fsp%3A) [2025](https://sp2025.ieee-security.org/program-papers.html), [2024](https://sp2024.ieee-security.org/program-papers.html), [2023](https://sp2023.ieee-security.org/program-papers.html), [2022](https://www.ieee-security.org/TC/SP2022/program-papers.html), [2019](https://www.ieee-security.org/TC/SP2019/program-papers.html) - [CCS](https://dblp.uni-trier.de/search?q=federate%20venue%3ACCS%3A) [2025](https://dl.acm.org/doi/proceedings/10.1145/3719027), [2024](https://dl.acm.org/doi/proceedings/10.1145/3658644), [2023](https://dl.acm.org/doi/proceedings/10.1145/3576915), [2022](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html), [2021](https://sigsac.org/ccs/CCS2021/accepted-papers.html), [2019](https://www.sigsac.org/ccs/CCS2019/index.php/program/accepted-papers/), [2017](https://acmccs.github.io/papers/) - [USENIX Security](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fuss%3A) [2025](https://www.usenix.org/conference/usenixsecurity25/technical-sessions), [2024](https://www.usenix.org/conference/usenixsecurity24/technical-sessions), [2023](https://www.usenix.org/conference/usenixsecurity23/technical-sessions), [2022](https://www.usenix.org/conference/usenixsecurity22/technical-sessions), [2020](https://www.usenix.org/conference/usenixsecurity20/technical-sessions) - [NDSS](https://dblp.uni-trier.de/search?q=federate%20venue%3ANDSS%3A) [2026](https://www.ndss-symposium.org/ndss2026/accepted-papers/), [2025](https://www.ndss-symposium.org/ndss2025/accepted-papers/), [2024](https://www.ndss-symposium.org/ndss2024/accepted-papers/), [2023](https://www.ndss-symposium.org/ndss2023/accepted-papers/), [2022](https://www.ndss-symposium.org/ndss2022/accepted-papers/), [2021](https://www.ndss-symposium.org/ndss2021/accepted-papers/)

fl in top secure conference and journal

### 2026 #### NDSS - A Unified Defense Framework Against Membership Inference in Federated Learning via Distillation and Contribution-Aware Aggregation. [[PUB](https://www.ndss-symposium.org/ndss-paper/a-unified-defense-framework-against-membership-inference-in-federated-learning-via-distillation-and-contribution-aware-aggregation/)] - Entente: Cross-silo Intrusion Detection on Network Log Graphs with Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/entente-cross-silo-intrusion-detection-on-network-log-graphs-with-federated-learning/)] - ZKSL: Verifiable and Efficient Split Federated Learning via Asynchronous Zero-Knowledge Proofs. [[PUB](https://www.ndss-symposium.org/ndss-paper/zksl-verifiable-and-efficient-split-federated-learning-via-asynchronous-zero-knowledge-proofs/)] - SVDefense: Effective Defense against Gradient Inversion Attacks via Singular Value Decomposition. [[PUB](https://www.ndss-symposium.org/ndss-paper/svdefense-effective-defense-against-gradient-inversion-attacks-via-singular-value-decomposition/)] ### 2025 #### CCS - Armadillo: Robust Single-Server Secure Aggregation for Federated Learning with Input Validation. [[PUB](https://doi.org/10.1145/3719027.3765216)] - FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning. [[PUB](https://doi.org/10.1145/3719027.3744883)] - Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization. [[PUB](https://doi.org/10.1145/3719027.3765044)] - On Hyperparameters and Backdoor-Resistance in Horizontal Federated Learning. [[PUB](https://doi.org/10.1145/3719027.3765211)] - Poster: Adaptive Gradient Clipping with Personalized Differential Privacy for Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1145/3719027.3760710)] - Secure Noise Sampling for Differentially Private Collaborative Learning. [[PUB](https://doi.org/10.1145/3719027.3744834)] #### USENIX Security - DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/gu-xiaolan)] - FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/diaa)] - From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/xu-xiangrui)] - PoiSAFL: Scalable Poisoning Attack Framework to Byzantine-resilient Semi-asynchronous Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/pang-xiaoyi)] - Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/fan-refiner)] - SoK: Gradient Inversion Attacks in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/carletti)] - SoK: On Gradient Leakage in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/du)] - From Purity to Peril: Backdooring Merged Models From "Harmless" Benign Components. [[PUB](https://www.usenix.org/conference/usenixsecurity25/presentation/wang-lijin)] #### S&P - Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/11023255)] - Practical Poisoning Attacks with Limited Byzantine Clients in Clustered Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/11023464)] - An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models. [[PUB](https://ieeexplore.ieee.org/document/11050826)] - Privacy-Preserving Mutual Authentication Protocol for Federated Learning in Intelligent Transportation Systems. [[PUB](https://ieeexplore.ieee.org/document/11050805)] - FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/11050846)] - Enhancing Jailbreak Resistance in Large Language Models Using Model Merge. [[PUB](https://doi.org/10.1109/SPW67851.2025.00015)] - On the Conflict Between Robustness and Learning in Collaborative Machine Learning. [[PUB](https://doi.org/10.1109/SP61157.2025.00249)] #### NDSS - Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models. [[PUB](https://www.ndss-symposium.org/ndss-paper/privacy-preserving-data-deduplication-for-enhancing-federated-learning-of-language-models/)] - Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction. [[PUB](https://www.ndss-symposium.org/ndss-paper/scale-mia-a-scalable-model-inversion-attack-against-secure-federated-learning-via-latent-space-reconstruction/)] [[CODE](https://github.com/unknown123489/Scale-MIA)] - URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/urvfl-undetectable-data-reconstruction-attack-on-vertical-federated-learning/)] [[CODE](https://github.com/duanyiyao/URVFL)] - RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation. [[PUB](https://www.ndss-symposium.org/ndss-paper/raifle-reconstruction-attacks-on-interaction-based-federated-learning-with-adversarial-data-manipulation/)] [[CODE](https://github.com/dzungvpham/raifle)] - CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling. [[PUB](https://www.ndss-symposium.org/ndss-paper/censor-defense-against-gradient-inversion-via-orthogonal-subspace-bayesian-sampling/)] ### 2024 #### USENIX Security - ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/xu-zhangchen)] - BackdoorIndicator: Leveraging OOD Data for Proactive Backdoor Detection in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/li-songze)] - Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/tan)] - Efficient Privacy Auditing in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/chang)] - FAMOS: Robust Privacy-Preserving Authentication on Payment Apps via Federated Multi-Modal Contrastive Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/cai-yifeng)] - Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/jiang-zhifeng)] - Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/lyu)] - Accelerating Secure Collaborative Machine Learning with Protocol-Aware RDMA. [[PUB](https://www.usenix.org/conference/usenixsecurity24/presentation/ren)] #### CCS - Byzantine-Robust Decentralized Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3670307)] - Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3670365)] - Cross-silo Federated Learning with Record-level Personalized Differential Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3670351)] - Samplable Anonymous Aggregation for Private Federated Data Analysis. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3690224)] - Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3690200)] - Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3690187)] [[CODE](https://github.com/Yuxin104/Opt-GDBA)] - Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3690191)] - Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3691411)] - Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. [[PUB](https://dl.acm.org/doi/10.1145/3658644.3691383)] - BadMerging: Backdoor Attacks Against Model Merging. [[PUB](https://doi.org/10.1145/3658644.3690284)] [[CODE](https://github.com/jzhang538/BadMerging)] - CoGNN: Towards Secure and Efficient Collaborative Graph Learning. [[PUB](https://doi.org/10.1145/3658644.3670300)] - Uncovering Gradient Inversion Risks in Practical Language Model Training. [[PUB](https://doi.org/10.1145/3658644.3690292)] #### NDSS - FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting. [[PUB](https://www.ndss-symposium.org/ndss-paper/fp-fed-privacy-preserving-federated-detection-of-browser-fingerprinting/)] - FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/freqfed-a-frequency-analysis-based-approach-for-mitigating-poisoning-attacks-in-federated-learning/)] - Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/automatic-adversarial-adaption-for-stealthy-poisoning-attacks-in-federated-learning/)] - CrowdGuard: Federated Backdoor Detection in Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/crowdguard-federated-backdoor-detection-in-federated-learning/)] - Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption. [[PUB](https://www.ndss-symposium.org/ndss-paper/pencil-private-and-extensible-collaborative-learning-without-the-non-colluding-assumption/)] #### S&P - Protecting Label Distribution in Cross-Silo Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10646748)] - FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks. [[PUB](https://ieeexplore.ieee.org/document/10646613)] - BadVFL: Backdoor Attacks in Vertical Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10646664)] - SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks. [[PUB](https://ieeexplore.ieee.org/document/10646830)] - Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation. [[PUB](https://ieeexplore.ieee.org/document/10646724)] - LayerDBA: Circumventing Similarity-Based Defenses in Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10795458/)] - Poster: Towards Privacy-Preserving Federated Recommendation via Synthetic Interactions. [[PUB](https://ieeexplore.ieee.org/document/10579513/)] - A Performance Analysis for Confidential Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10579526)] ### 2023 #### CCS - Turning Privacy-preserving Mechanisms against Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623114)] [[PDF](https://arxiv.org/abs/2305.05355)] - MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623212)] - martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623134)] [[PDF](https://arxiv.org/abs/2309.01098)] [[CODE](https://github.com/liqi16/martfl)] - Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623193)] [[PDF](https://arxiv.org/abs/2209.04030)] - Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3624371)] - Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3624371)] - Turning Privacy-preserving Mechanisms against Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3576915.3623114)] [[PDF](https://arxiv.org/abs/2305.05355)] [[CODE](https://github.com/DCALab-UNIPV/Turning-Privacy-preserving-Mechanisms-against-Federated-Learning)] #### USENIX Security - Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/mozaffari)] [[PDF](https://arxiv.org/abs/2110.04350)] - PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/yang-yuchen)] [[CODE](https://github.com/BHui97/PrivateFL)] - Gradient Obfuscation Gives a False Sense of Security in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/yue)] [[PDF](https://arxiv.org/abs/2206.04055)] [[CODE](https://github.com/KAI-YUE/rog)] - FedVal: Different good or different bad in federated learning. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/valadi)] [[PDF](https://arxiv.org/abs/2306.04040)] [[CODE](https://github.com/viktorvaladi/fedval)] - HOLMES: Efficient Distribution Testing for Secure Collaborative Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity23/presentation/chang)] #### NDSS - Securing Federated Sensitive Topic Classification against Poisoning Attacks. [[PUB](https://www.ndss-symposium.org/ndss-paper/securing-federated-sensitive-topic-classification-against-poisoning-attacks/)] [[PDF](https://arxiv.org/abs/2201.13086)] [[CODE](https://github.com/FRM-Sec/FRM)] - PPA: Preference Profiling Attack Against Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/ppa-preference-profiling-attack-against-federated-learning/)] [[PDF](https://arxiv.org/abs/2202.04856)] #### S&P - FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information. [[PUB](https://www.computer.org/csdl/proceedings-article/sp/2023/933600a326/1He7Y3q8FMY)] [[PDF](https://arxiv.org/abs/2210.10936)] - Scalable and Privacy-Preserving Federated Principal Component Analysis. [[PUB](https://ieeexplore.ieee.org/document/10179350)] [[PDF](https://arxiv.org/abs/2304.00129)] - BayBFed: Bayesian Backdoor Defense for Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10179362)] [[PDF](https://arxiv.org/abs/2301.09508)] - 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10179401)] [[CODE](https://github.com/haoyangliASTAPLE/3DFed)] - RoFL: Robustness of Secure Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10179400)] [[PDF](https://arxiv.org/abs/2107.03311)] [[CODE](https://github.com/pps-lab/rofl-project-code)] - Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10179434)] [[CODE](https://github.com/eniac/flamingo)] - ELSA: Secure Aggregation for Federated Learning with Malicious Actors. - Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy. [[PUB](https://www.computer.org/csdl/proceedings-article/sp/2023/933600a076/1He7XMLcnsc)] [[PDF](https://arxiv.org/abs/2208.08662)] - SafeFL: MPC-friendly Framework for Private and Robust Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10188630)] - On the Pitfalls of Security Evaluation of Robust Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10188636)] - ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems. [[PUB](https://doi.org/10.1109/SP46215.2023.10179446)] - ELSA: Secure Aggregation for Federated Learning with Malicious Actors. [[PUB](https://doi.org/10.1109/SP46215.2023.10179468)] ### 2022 #### CCS - CERBERUS: Exploring Federated Prediction of Security Events. [[PUB](https://dl.acm.org/doi/10.1145/3548606.3560580)] [[PDF](https://arxiv.org/abs/2209.03050)] - EIFFeL: Ensuring Integrity for Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3548606.3560611)] [[PDF](https://arxiv.org/abs/2112.12727)] - Eluding Secure Aggregation in Federated Learning via Model Inconsistency. [[PUB](https://dl.acm.org/doi/10.1145/3548606.3560557)] [[PDF](https://arxiv.org/abs/2111.07380)] [[CODE](https://github.com/pasquini-dario/eludingsecureaggregation)] - Federated Boosted Decision Trees with Differential Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3548606.3560687)] [[PDF](https://arxiv.org/abs/2210.02910)] [[CODE](https://github.com/Samuel-Maddock/federated-boosted-dp-trees)] #### S&P - Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/9833647/)] [[VIDEO](https://www.youtube.com/watch?v=tQv3CpxIyvs)] - SNARKBlock: Federated Anonymous Blocklisting from Hidden Common Input Aggregate Proofs. [[PUB](https://doi.org/10.1109/SP46214.2022.9833656)] #### USENIX Security - SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost. [[PUB](https://www.usenix.org/conference/usenixsecurity22/presentation/chandran)] [[PDF](https://eprint.iacr.org/2021/1538)] [[CODE](https://github.com/shahakash28/simc)] [[VIDEO](https://www.youtube.com/watch?v=0Oaqi0JHUac)] [[SUPP](https://www.usenix.org/system/files/usenixsecurity22-chandran.pdf)] - Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors. [[PUB](https://www.usenix.org/conference/usenixsecurity22/presentation/stevens)] [[SLIDE](https://www.usenix.org/system/files/sec22_slides-stevens.pdf)] [[VIDEO](https://www.youtube.com/watch?v=9kYHQkr6DuE)] - Label Inference Attacks Against Vertical Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity22/presentation/fu-chong)] [[SLIDE](https://www.usenix.org/system/files/sec22_slides-fu-chong.pdf)] [[CODE](https://github.com/FuChong-cyber/label-inference-attacks)] [[VIDEO](https://www.youtube.com/watch?v=JEmRbDtosVw)] - FLAME: Taming Backdoors in Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity22/presentation/nguyen)] [[SLIDE](https://www.usenix.org/system/files/sec22_slides-nguyen.pdf)] [[PDF](https://arxiv.org/abs/2101.02281)] [[VIDEO](https://www.youtube.com/watch?v=nMrte2S9U68)] #### NDSS - Local and Central Differential Privacy for Robustness and Privacy in Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/auto-draft-204/)] [[PDF](https://arxiv.org/abs/2009.03561)] [[VIDEO](https://www.youtube.com/watch?v=_aH2j5A3608&list=PLfUWWM-POgQulyX2vzKzUtZEkVn1M9G2a&index=3)] [[UC.](https://github.com/wenzhu23333/Differential-Privacy-Based-Federated-Learning)] - Interpretable Federated Transformer Log Learning for Cloud Threat Forensics. [[PUB](https://www.ndss-symposium.org/ndss-paper/auto-draft-236/)] [[VIDEO](https://www.youtube.com/watch?v=3HoysA6hsC8&list=PLfUWWM-POgQsS08uHJUJI6sawDO_3sNh0&index=3)] [[UC.](https://github.com/cyberthreat-datasets/ctdd-2021-os-syslogs)] - FedCRI: Federated Mobile Cyber-Risk Intelligence. [[PUB](https://www.ndss-symposium.org/ndss-paper/auto-draft-229/)] [[VIDEO](https://www.youtube.com/watch?v=2zmdPqCCFxg&list=PLfUWWM-POgQs8ZZMMCX1RoNnmSQ70QXxd&index=3)] - DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection. [[PUB](https://www.ndss-symposium.org/ndss-paper/auto-draft-205/)] [[PDF](https://arxiv.org/abs/2201.00763)] [[VIDEO](https://www.youtube.com/watch?v=MJF_7vnoGh4&list=PLfUWWM-POgQulyX2vzKzUtZEkVn1M9G2a&index=4)] ### 2021 #### CCS - Private Hierarchical Clustering in Federated Networks. [[PUB](https://dl.acm.org/doi/10.1145/3460120.3484822)] [[PDF](https://arxiv.org/abs/2105.09057)] #### NDSS - FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. [[PUB](https://www.ndss-symposium.org/ndss-paper/fltrust-byzantine-robust-federated-learning-via-trust-bootstrapping/)] [[PDF](https://arxiv.org/abs/2012.13995)] [[CODE](https://people.duke.edu/~zg70/code/fltrust.zip)] [[VIDEO](https://www.youtube.com/watch?v=zhhdPgKPCN0&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=2)] [[SLIDE](https://people.duke.edu/~zg70/code/Secure_Federated_Learning.pdf)] - POSEIDON: Privacy-Preserving Federated Neural Network Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/poseidon-privacy-preserving-federated-neural-network-learning/)] [[VIDEO](https://www.youtube.com/watch?v=kX6-PMzxZ3c&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=1)] - Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning. [[PUB](https://www.ndss-symposium.org/ndss-paper/manipulating-the-byzantine-optimizing-model-poisoning-attacks-and-defenses-for-federated-learning/)] [[CODE](https://github.com/vrt1shjwlkr/NDSS21-Model-Poisoning)] [[VIDEO](https://www.youtube.com/watch?v=G2VYRnLqAXE&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=3)] #### s&p - SAFELearn: Secure Aggregation for private FEderated Learning. [[PUB](https://doi.org/10.1109/SPW53761.2021.00017)] #### S&P Workshop - SAFELearn: Secure Aggregation for private FEderated Learning. [[PUB](https://ieeexplore.ieee.org/document/9474309)] #### usenix security - Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity21/presentation/zheng)] ### 2020 #### ndss - Strong Authentication without Temper-Resistant Hardware and Application to Federated Identities. [[PUB](https://www.ndss-symposium.org/ndss-paper/strong-authentication-without-temper-resistant-hardware-and-application-to-federated-identities/)] #### s&p - The Value of Collaboration in Convex Machine Learning with Differential Privacy. [[PUB](https://doi.org/10.1109/SP40000.2020.00025)] #### USENIX Security - Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. [[PUB](https://www.usenix.org/conference/usenixsecurity20/presentation/fang)] [[PDF](https://arxiv.org/abs/1911.11815)] [[CODE](https://people.duke.edu/~zg70/code/fltrust.zip)] [[VIDEO](https://www.youtube.com/watch?v=SQ12UpYrUVU&feature=emb_imp_woyt)] [[SLIDE](https://www.usenix.org/system/files/sec20_slides_fang.pdf)] ### 2019 #### CCS - A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain. [[PUB](https://dl.acm.org/doi/10.1145/3319535.3363256)] - Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain. [[PUB](https://doi.org/10.1145/3319535.3363256)] #### S&P - Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning :fire:. [[PUB](https://www.computer.org/csdl/proceedings-article/sp/2019/666000a739/1dlwhtj4r7O)] [[VIDEO](https://youtu.be/lzJY4BjCxTc)] [[SLIDE](https://www.ieee-security.org/TC/SP2019/SP19-Slides-pdfs/Milad_Nasr_-_08-Milad_Nasr-Comprehensive_Privacy_Analysis_of_Deep_Learning_)] [[CODE](https://github.com/privacytrustlab/ml_privacy_meter)] - IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/8844592)] - Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. [[PUB](https://doi.org/10.1109/SP.2019.00065)] - Exploiting Unintended Feature Leakage in Collaborative Learning. [[PUB](https://doi.org/10.1109/SP.2019.00029)] #### usenix security - Triton: A Software-Reconfigurable Federated Avionics Testbed. [[PUB](https://www.usenix.org/conference/cset19/presentation/crow)] ### 2018 #### ccs - The Price of Privacy in Collaborative Learning. [[PUB](https://doi.org/10.1145/3243734.3278525)] ### 2017 #### CCS - Practical Secure Aggregation for Privacy Preserving Machine Learning. [[PUB](https://dl.acm.org/doi/10.1145/3133956.3133982)] [[PDF](https://eprint.iacr.org/2017/281)] [[解读](https://zhuanlan.zhihu.com/p/445656765)] [[UC.](https://github.com/Chen-Junbao/SecureAggregation)] [[UC](https://github.com/corentingiraud/federated-learning-secure-aggregation)] - Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. [[PUB](https://doi.org/10.1145/3133956.3134012)] ### 2015 #### s&p - Privacy by Design in Federated Identity Management. [[PUB](https://doi.org/10.1109/SPW.2015.24)] ### 2014 #### ndss - Hardening Persona - Improving Federated Web Login. [[PUB](https://www.ndss-symposium.org/ndss2014/hardening-persona-improving-federated-web-login)]

## fl in top cv conference and journal Federated Learning papers accepted by top CV(computer vision) conference and journal, Including [CVPR](https://dblp.uni-trier.de/db/conf/cvpr/index.html)(Computer Vision and Pattern Recognition), [ICCV](https://dblp.uni-trier.de/db/conf/iccv/index.html)(IEEE International Conference on Computer Vision), [ECCV](https://dblp.uni-trier.de/db/conf/eccv/index.html)(European Conference on Computer Vision), [MM](https://dblp.org/db/conf/mm/index.html)(ACM International Conference on Multimedia), [IJCV](https://dblp.uni-trier.de/db/journals/ijcv/index.html)(International Journal of Computer Vision). - [CVPR](https://dblp.uni-trier.de/search?q=federate%20venue%3ACVPR%3A) [2025](https://openaccess.thecvf.com/CVPR2025?day=all), [2024](https://openaccess.thecvf.com/CVPR2024?day=all), [2023](https://openaccess.thecvf.com/CVPR2023?day=all), [2022](https://openaccess.thecvf.com/CVPR2022), [2021](https://openaccess.thecvf.com/CVPR2021?day=all) - [ICCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AICCV%3A) [2023](https://openaccess.thecvf.com/ICCV2023?day=all), [2021](https://openaccess.thecvf.com/ICCV2021?day=all) - [ECCV](https://dblp.uni-trier.de/search?q=federate%20venue%3AECCV%3A) [2024](https://www.ecva.net/papers.php), [2022](https://www.ecva.net/papers.php), [2020](https://www.ecva.net/papers.php) - [MM](https://dblp.uni-trier.de/search?q=federated%20streamid%3Aconf%2Fmm%3A) [2025](https://dl.acm.org/doi/proceedings/10.1145/3746027), [2024](https://dl.acm.org/doi/proceedings/10.1145/3664647), [2023](https://dl.acm.org/doi/proceedings/10.1145/3581783), [2022](https://dblp.uni-trier.de/db/conf/mm/mm2022.html), [2021](https://2021.acmmm.org/main-track-list), [2020](https://2020.acmmm.org/main-track-list.html) - [IJCV](https://dblp.uni-trier.de/search?q=federate%20streamid%3Ajournals%2Fijcv%3A) 2025, 2024

fl in top cv conference and journal

### 2026 #### ijcv - Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization. [[PUB](https://doi.org/10.1007/s11263-026-02777-4)] - CoSurfGS: 3D Surface Gaussian Splatting with Collaborative Distributed Learning for Large-scale Scene Reconstruction. [[PUB](https://doi.org/10.1007/s11263-025-02627-9)] ### 2025 #### iccv - A Framework for Double-Blind Federated Adaptation of Foundation Models. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00094)] [[CODE](https://github.com/tnurbek/blindfed)] - Class-Wise Federated Averaging for Efficient Personalization. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00173)] - Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00141)] [[CODE](https://github.com/LINs-lab/client2vec)] - Cooperative Pseudo Labeling for Unsupervised Federated Classification. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00318)] [[CODE](https://github.com/krumpguo/FedCoPL)] - EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00177)] - FDPT: Federated Discrete Prompt Tuning for Black-Box Visual-Language Models. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00237)] - FedAGC: Federated Continual Learning with Asymmetric Gradient Correction. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00366)] - FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00355)] [[CODE](https://github.com/hwang52/FedDifRC)] - Federated Continual Instruction Tuning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00131)] [[CODE](https://github.com/Ghy0501/FCIT)] - Federated Continuous Category Discovery and Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00234)] - Federated Domain Generalization with Domain-Specific Soft Prompts Generation. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00228)] - Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00375)] - Federated Representation Angle Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00130)] - FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00209)] - FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01660)] [[CODE](https://github.com/mainaksingha01/FedMVP)] - FedPall: Prototype-Based Adversarial and Collaborative Learning for Federated Learning with Feature Drift. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00298)] [[CODE](https://github.com/DistriAI/FedPall)] - FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00649)] - FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00439)] - FedXDS: Leveraging Model Attribution Methods to Counteract Data Heterogeneity in Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00435)] - Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00378)] - FLSeg: Enhancing Privacy and Robustness in Federated Learning under Heterogeneous Data via Model Segmentation. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00373)] - Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00145)] [[CODE](https://github.com/zhentian777/FUCRT)] - Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00261)] - Latte: Collaborative Test-Time Adaptation of Vision-Language Models in Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00020)] [[CODE](https://github.com/baowenxuan/Latte)] - LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00356)] - Neural Architecture Search Driven by Locally Guided Diffusion for Personalized Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00402)] - Personalized Federated Learning Under Local Supervision. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00388)] - Sibai: A Few-Shot Meta-Classifier for Poisoning Detection in Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00361)] - Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00285)] - Stealthy Backdoor Attack in Federated Learning via Adaptive Layer-Wise Gradient Alignment. [[PUB](https://doi.org/10.1109/ICCV51701.2025.02708)] - Task-Aware Prompt Gradient Projection for Parameter-Efficient Tuning Federated Class-Incremental Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00253)] - Tensor-Aggregated LoRA in Federated Fine-Tuning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00106)] - Towards Privacy-preserved Pre-training of Remote Sensing Foundation Models with Federated Mutual-Guidance Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00176)] - You are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-Tailed Data. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00264)] [[CODE](https://github.com/shanss132/FedYoYo)] - COME: Dual Structure-Semantic Learning with Collaborative MOE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01993)] - Disrupting Model Merging: A Parameter-Level Defense without Sacrificing Accuracy. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01644)] [[CODE](https://github.com/ISCT-W/PaRaMS)] - Free-Merging: Fourier Transform for Efficient Model Merging. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00368)] [[CODE](https://github.com/Zhengsh123/FREE-Merging)] - FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization. [[PUB](https://doi.org/10.1109/ICCV51701.2025.00324)] - Task Vector Quantization for Memory-Efficient Model Merging. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01870)] - Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning. [[PUB](https://doi.org/10.1109/ICCV51701.2025.01176)] [[CODE](https://github.com/KongLingqi2333/WSL-VIReID)] #### MM - Client-Server Co-design with Multi-modal Codebooks Makes Better and Faster Federate Knowledge Sharing. [[PUB](https://doi.org/10.1145/3746027.3755311)] - Consistency of Local and Global Flatness for Federated Learning. [[PUB](https://doi.org/10.1145/3746027.3755226)] [[CODE](https://github.com/junkangLiu0/FedNSAM)] - Discovering Maximum Frequency Consensus: Lightweight Federated Learning for Medical Image Segmentation. [[PUB](https://doi.org/10.1145/3746027.3755528)] - Diverse and Public Features Cooperation via Gradient Rectification for Federated Prompt Learning. [[PUB](https://doi.org/10.1145/3746027.3755308)] - DualFPT: Handling Data Heterogeneity in Federated Prompt Tuning from both Generalized and Personalized Perspective. [[PUB](https://doi.org/10.1145/3746027.3754872)] - DynFed: Adaptive Federated Learning via Quantization-Aware Knowledge Distillation. [[PUB](https://doi.org/10.1145/3746027.3755451)] - FeatShield: Isolating Malicious Feature Extractors for Backdoor-Robust Federated Learning. [[PUB](https://doi.org/10.1145/3746027.3755742)] - FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models. [[PUB](https://doi.org/10.1145/3746027.3755387)] - FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning. [[PUB](https://doi.org/10.1145/3746027.3754814)] - FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning. [[PUB](https://doi.org/10.1145/3746027.3754587)] - Federated Incomplete Multi-view Clustering with Individual Structure Preservation and Central Representation Tensorization. [[PUB](https://doi.org/10.1145/3746027.3755799)] [[CODE](https://github.com/LiYannnnnudt/FIMC)] - FedRog: Robust Federated Graph Classification for Strong Heterogeneity and High-Noise Scenarios. [[PUB](https://doi.org/10.1145/3746027.3755358)] - FORGET ME: Federated Unlearning for Face Generation Models. [[PUB](https://doi.org/10.1145/3746027.3754935)] [[CODE](https://github.com/FanQi-AI/FFGU)] - Multi-Width Neural Network-Assisted Hierarchical Federated Learning in Heterogeneous Cloud-Edge-Device Computing. [[PUB](https://doi.org/10.1145/3746027.3754596)] - Positive Style Accumulation: A Style Screening and Continuous Utilization Framework for Federated DG-ReID. [[PUB](https://doi.org/10.1145/3746027.3755549)] - PriCAF: Privacy-Preserving Contribution Assessment in Federated Learning Before Model Training. [[PUB](https://doi.org/10.1145/3746027.3755825)] - Device-Cloud Collaborative Learning Framework for Efficient Unknown Object Detection. [[PUB](https://doi.org/10.1145/3746027.3755681)] - Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning. [[PUB](https://doi.org/10.1145/3746027.3755703)] - Multi-view Collaborative Representation Learning from Noisy Labels for VHR Imagery Classification. [[PUB](https://doi.org/10.1145/3746027.3755839)] - Outlier-Aware Model Merging for Efficient Multitask Inference. [[PUB](https://doi.org/10.1145/3746027.3754894)] - Spatial-Frequency Mamba Collaborative Learning Network for Infrared Small Target Detection. [[PUB](https://doi.org/10.1145/3746027.3754572)] - Task Arithmetic in Trust Region: A Training-Free Model Merging Approach to Navigate Knowledge Conflicts. [[PUB](https://doi.org/10.1145/3746027.3755789)] - Tractography-Guided Dual-Label Collaborative Learning for Multi-Modal Cranial Nerves Parcellation. [[PUB](https://doi.org/10.1145/3746027.3755513)] #### CVPR - Federated Learning with Domain Shift Eraser. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Federated_Learning_with_Domain_Shift_Eraser_CVPR_2025_paper.html)] - Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Caldarola_Beyond_Local_Sharpness_Communication-Efficient_Global_Sharpness-aware_Minimization_for_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/pietrocagnasso/fedgloss)] - FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Chen_FedBiP_Heterogeneous_One-Shot_Federated_Learning_with_Personalized_Latent_Diffusion_Models_CVPR_2025_paper.html)] [[CODE](https://github.com/HaokunChen245/FedBiP)] - FedCS: Coreset Selection for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Hao_FedCS_Coreset_Selection_for_Federated_Learning_CVPR_2025_paper.html)] - AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/He_AFL_A_Single-Round_Analytic_Approach_for_Federated_Learning_with_Pre-trained_CVPR_2025_paper.html)] [[CODE](https://github.com/ZHUANGHP/Analytic-federated-learning)] - NoT: Federated Unlearning via Weight Negation. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Khalil_NoT_Federated_Unlearning_via_Weight_Negation_CVPR_2025_paper.html)] - Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Kumar_Fortifying_Federated_Learning_Towards_Trustworthiness_via_Auditable_Data_Valuation_and_CVPR_2025_paper.html)] - Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Li_Infighting_in_the_Dark_Multi-Label_Backdoor_Attack_in_Federated_Learning_CVPR_2025_paper.html)] - Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Mind_the_Gap_Confidence_Discrepancy_Can_Guide_Federated_Semi-Supervised_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/Jay-Codeman/SAGE)] - Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Geometric_Knowledge-Guided_Localized_Global_Distribution_Alignment_for_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/WeiDai-David/2025CVPR_GGEUR)] - HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Raswa_HistoFS_Non-IID_Histopathologic_Whole_Slide_Image_Classification_via_Federated_Style_CVPR_2025_paper.html)] [[COCE](https://lalakitchen.github.io/HistoFS/)] - F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Saha_F3OCUS_-_Federated_Finetuning_of_Vision-Language_Foundation_Models_with_Optimal_CVPR_2025_paper.html)] [[PAGE](https://pramitsaha.github.io/FOCUS/)] - FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Shi_FedAWA_Adaptive_Optimization_of_Aggregation_Weights_in_Federated_Learning_Using_CVPR_2025_paper.html)] - FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Tan_FedSPA_Generalizable_Federated_Graph_Learning_under_Homophily_Heterogeneity_CVPR_2025_paper.html)] [[CODE](https://github.com/OakleyTan/FedSPA)] - Population Normalization for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Population_Normalization_for_Federated_Learning_CVPR_2025_paper.html)] - Model Poisoning Attacks to Federated Learning via Multi-Round Consistency. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Model_Poisoning_Attacks_to_Federated_Learning_via_Multi-Round_Consistency_CVPR_2025_paper.html)] [[CODE](https://github.com/xyq7/PoisonedFL/)] - dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Xie_dFLMoE_Decentralized_Federated_Learning_via_Mixture_of_Experts_for_Medical_CVPR_2025_paper.html)] - Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Detecting_Backdoor_Attacks_in_Federated_Learning_via_Direction_Alignment_Inspection_CVPR_2025_paper.html)] [[CODE](https://github.com/JiiahaoXU/AlignIns)] - A Simple Data Augmentation for Feature Distribution Skewed Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Yan_A_Simple_Data_Augmentation_for_Feature_Distribution_Skewed_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/IAMJackYan/FedRDN)] - Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Handling_Spatial-Temporal_Data_Heterogeneity_for_Federated_Continual_Learning_via_Tail_CVPR_2025_paper.html)] - Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Subspace_Constraint_and_Contribution_Estimation_for_Heterogeneous_Federated_Learning_CVPR_2025_paper.html)] [[CODE](https://github.com/AVC2-UESTC/FedSCE.git)] - pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_pFedMxF_Personalized_Federated_Class-Incremental_Learning_with_Mixture_of_Frequency_Aggregation_CVPR_2025_paper.html)] - FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_FedCALM_Conflict-aware_Layer-wise_Mitigation_for_Selective_Aggregation_in_Deeper_Personalized_CVPR_2025_paper.html)] - Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Unlearning_through_Knowledge_Overwriting_Reversible_Federated_Unlearning_via_Selective_Sparse_CVPR_2025_paper.html)] - FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.html)] [[CODE](https://github.com/Liar-Mask/FedMIA)] - Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Patient-Level_Anatomy_Meets_Scanning-Level_Physics_Personalized_Federated_Low-Dose_CT_Denoising_CVPR_2025_paper.html)] - FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.html)] [[CODE](https://github.com/Liar-Mask/FedMIA)] - AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Du_AdaMMS_Model_Merging_for_Heterogeneous_Multimodal_Large_Language_Models_with_CVPR_2025_paper.html)] - Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Decouple-Then-Merge_Finetune_Diffusion_Models_as_Multi-Task_Learning_CVPR_2025_paper.html)] - Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Embracing_Collaboration_Over_Competition_Condensing_Multiple_Prompts_for_Visual_In-Context_CVPR_2025_paper.html)] [[CODE](https://github.com/gimpong/CVPR25-Condenser)] - Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Sami_Gradient_Inversion_Attacks_on_Parameter-Efficient_Fine-Tuning_CVPR_2025_paper.html)] [[CODE](https://github.com/info-ucr/PEFTLeak)] - How to Merge Your Multimodal Models Over Time?. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Dziadzio_How_to_Merge_Your_Multimodal_Models_Over_Time_CVPR_2025_paper.html)] - Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Learning_Dynamic_Collaborative_Network_for_Semi-supervised_3D_Vessel_Segmentation_CVPR_2025_paper.html)] [[CODE](https://github.com/xujiaommcome/DiCo)] - Less is More: Efficient Model Merging with Binary Task Switch. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Qi_Less_is_More_Efficient_Model_Merging_with_Binary_Task_Switch_CVPR_2025_paper.html)] - Libra-Merging: Importance-redundancy and Pruning-merging Trade-off for Acceleration Plug-in in Large Vision-Language Model. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Libra-Merging_Importance-redundancy_and_Pruning-merging_Trade-off_for_Acceleration_Plug-in_in_Large_CVPR_2025_paper.html)] [[CODE](https://github.com/longrongyang/Libra-Merging)] - OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Tang_OnlineAnySeg_Online_Zero-Shot_3D_Segmentation_by_Visual_Foundation_Model_Guided_CVPR_2025_paper.html)] - PLeaS - Merging Models with Permutations and Least Squares. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Nasery_PLeaS_-_Merging_Models_with_Permutations_and_Least_Squares_CVPR_2025_paper.html)] - PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zou_PromptHashAffinity-Prompted_Collaborative_Cross-Modal_Learning_for_Adaptive_Hashing_Retrieval_CVPR_2025_paper.html)] [[CODE](https://github.com/ShiShuMo/PromptHash)] - Task Singular Vectors: Reducing Task Interference in Model Merging. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Gargiulo_Task_Singular_Vectors_Reducing_Task_Interference_in_Model_Merging_CVPR_2025_paper.html)] - Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Visual_and_Semantic_Prompt_Collaboration_for_Generalized_Zero-Shot_Learning_CVPR_2025_paper.html)] - Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models. [[PUB](https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Weakly_Supervised_Temporal_Action_Localization_via_Dual-Prior_Collaborative_Learning_Guided_CVPR_2025_paper.html)] #### IJCV - Relation-Guided Versatile Regularization for Federated Semi-Supervised Learning. [[PUB](https://link.springer.com/article/10.1007/s11263-024-02330-1)] - Achieving Procedure-Aware Instructional Video Correlation Learning Under Weak Supervision from a Collaborative Perspective. [[PUB](https://doi.org/10.1007/s11263-024-02272-8)] - HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning. [[PUB](https://doi.org/10.1007/s11263-024-02318-x)] [[CODE](https://github.com/ZengxiZhang/HUPE)] - Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID. [[PUB](https://doi.org/10.1007/s11263-025-02461-z)] [[CODE](https://github.com/FranklinLingfeng/code-for-SALCR)] ### 2024 #### MM - DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations. [[PUB](https://doi.org/10.1145/3664647.3681260)] [[CODE](https://github.com/GuogangZhu/DualFed)] - One-shot-but-not-degraded Federated Learning. [[PUB](https://doi.org/10.1145/3664647.3680715)] [[CODE](https://github.com/zenghui9977/IntactOFL)] - Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning. [[PUB](https://doi.org/10.1145/3664647.3681384)] [[CODE](https://github.com/SkyOfBeginning/FedCBC)] - FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models. [[PUB](https://doi.org/10.1145/3664647.3681490)] - Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition. [[PUB](https://doi.org/10.1145/3664647.3681588)] [[CODE](https://github.com/XinghaoWu/FedDecomp)] - CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation. [[PUB](https://doi.org/10.1145/3664647.3680867)] - Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training. [[PUB](https://doi.org/10.1145/3664647.3680733)] - FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework. [[PUB](https://doi.org/10.1145/3664647.3681545)] - One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity. [[PUB](https://doi.org/10.1145/3664647.3681054)] [[CODE](https://github.com/NaiboWang/FedELMY)] - FedSLS: Exploring Federated Aggregation in Saliency Latent Space. [[PUB](https://doi.org/10.1145/3664647.3681278)] - Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia. [[PUB](https://doi.org/10.1145/3664647.3680788)] - FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3664647.3681094)] - Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data. [[PUB](https://dl.acm.org/doi/10.1145/3664647.3681480)] - Cross-Modal Meta Consensus for Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1145/3664647.3681510)] - Masked Random Noise for Communication-Efficient Federated Learning. [[PUB](https://doi.org/10.1145/3664647.3680608)] - Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations. [[PUB](https://doi.org/10.1145/3664647.3681302)] - Adaptive Hierarchical Aggregation for Federated Object Detection. [[PUB](https://doi.org/10.1145/3664647.3681158)] - FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement. [[PUB](https://doi.org/10.1145/3664647.3681319)] - Federated Fuzzy C-means with Schatten-p Norm Minimization. [[PUB](https://doi.org/10.1145/3664647.3681557)] - Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation. [[PUB](https://doi.org/10.1145/3664647.3681415)] - CoPL: Parameter-Efficient Collaborative Prompt Learning for Audio-Visual Tasks. [[PUB](https://doi.org/10.1145/3664647.3681492)] #### IJCV - Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification. [[PUB](https://link.springer.com/article/10.1007/s11263-024-02077-9)] [[CODE](https://github.com/Zi-YuanYang/PSFed-Palm)] #### ECCV - SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72655-2_17)] [[CODE](https://github.com/KoalaYan/SkyMask)] - FedHide: Federated Learning by Hiding in the Neighbors. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72897-6_23)] - FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73668-1_14)] - FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73195-2_20)] - Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73030-6_10)] - Federated Learning with Local Openset Noisy Labels. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72754-2_3)] - FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73010-8_22)] - Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73004-7_11)] - BAFFLE: A Baseline of Backpropagation-Free Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73226-3_6)] [[CODE](https://github.com/FengHZ/BAFFLE)] - PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73650-6_9)] [[CODE](https://github.com/Ghy0501/PILoRA)] - Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72633-0_14)] - Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73229-4_2)] - FedHARM: Harmonizing Model Architectural Diversity in Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73036-8_3)] - SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72986-7_10)] - Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72952-2_8)] - Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73404-5_18)] - Towards Multi-modal Transformers in Federated Learning. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72633-0_13)] - Local and Global Flatness for Federated Domain Generalization. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73010-8_5)] - Feature Diversification and Adaptation for Federated Domain Generalization. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-73220-1_4)] - PFEDEDIT: Personalized Federated Learning via Automated Model Editing. [[PUB](https://link.springer.com/chapter/10.1007/978-3-031-72986-7_6)] - CoLeaF: A Contrastive-Collaborative Learning Framework for Weakly Supervised Audio-Visual Video Parsing. [[PUB](https://doi.org/10.1007/978-3-031-73247-8_1)] - Diffusion Soup: Model Merging for Text-to-Image Diffusion Models. [[PUB](https://doi.org/10.1007/978-3-031-73036-8_15)] - MAGMAX: Leveraging Model Merging for Seamless Continual Learning. [[PUB](https://doi.org/10.1007/978-3-031-73013-9_22)] - Model Breadcrumbs: Scaling Multi-task Model Merging with Sparse Masks. [[PUB](https://doi.org/10.1007/978-3-031-73226-3_16)] - Multi-branch Collaborative Learning Network for 3D Visual Grounding. [[PUB](https://doi.org/10.1007/978-3-031-72952-2_22)] - Training-Free Model Merging for Multi-target Domain Adaptation. [[PUB](https://doi.org/10.1007/978-3-031-72970-6_24)] #### CVPR - FedHCA2: Towards Hetero-Client Federated Multi-Task Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Lu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lu_FedHCA2_Towards_Hetero-Client_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2311.13250)] [[CODE](https://github.com/innovator-zero/FedHCA2)] - Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Fair_Federated_Learning_under_Domain_Skew_with_Local_Consistency_and_CVPR_2024_paper.html)] [[PDF](http://arxiv.org/abs/2405.16585)] [[CODE](https://github.com/yuhangchen0/FedHEAL)] - Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Think_Twice_Before_Selection_Federated_Evidential_Active_Learning_for_Medical_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Think_Twice_Before_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2312.02567)] [[CODE](https://github.com/JiayiChen815/FEAL)] - FedMef: Towards Memory-efficient Federated Dynamic Pruning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Huang_FedMef_Towards_Memory-efficient_Federated_Dynamic_Pruning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_FedMef_Towards_Memory-efficient_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.14737)] - Communication-Efficient Federated Learning with Accelerated Client Gradient. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Communication-Efficient_Federated_Learning_with_Accelerated_Client_Gradient_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Communication-Efficient_Federated_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2201.03172)] [[CODE](https://github.com/geehokim/FedACG)] - Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Kumar_Revamping_Federated_Learning_Security_from_a_Defenders_Perspective_A_Unified_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kumar_Revamping_Federated_Learning_CVPR_2024_supplemental.pdf)] [[CODE](https://github.com/NaveenKumar-1311/FCD)] - Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Qi_Adaptive_Hyper-graph_Aggregation_for_Modality-Agnostic_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qi_Adaptive_Hyper-graph_Aggregation_CVPR_2024_supplemental.pdf)] [[CODE](https://github.com/MM-Fed/HAMFL)] - Towards Efficient Replay in Federated Incremental Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Li_Towards_Efficient_Replay_in_Federated_Incremental_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Towards_Efficient_Replay_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.05890)] - Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Mixed-Precision_Quantization_for_Federated_Learning_on_Resource-Constrained_Heterogeneous_Devices_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Mixed-Precision_Quantization_for_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2311.18129)] - Data Valuation and Detections in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Li_Data_Valuation_and_Detections_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Data_Valuation_and_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2311.05304)] [[CODE](https://github.com/muz1lee/motdata)] - Decentralized Directed Collaboration for Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Decentralized_Directed_Collaboration_for_Personalized_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Decentralized_Directed_Collaboration_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2405.17876)] - Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Deng_Unlocking_the_Potential_of_Prompt-Tuning_in_Bridging_Generalized_and_Personalized_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Deng_Unlocking_the_Potential_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2310.18285)] [[CODE](https://github.com/ubc-tea/SGPT)] - Global and Local Prompts Cooperation via Optimal Transport for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Li_Global_and_Local_Prompts_Cooperation_via_Optimal_Transport_for_Federated_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Global_and_Local_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.00041)] [[CODE](https://github.com/hongxialee/fedotp)] - Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Liao_Rethinking_the_Representation_in_Federated_Unsupervised_Learning_with_Non-IID_Data_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liao_Rethinking_the_Representation_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.16398)] [[CODE](https://github.com/XeniaLLL/FedU2)] - Relaxed Contrastive Learning for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Seo_Relaxed_Contrastive_Learning_for_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Seo_Relaxed_Contrastive_Learning_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2401.04928)] [[CODE](https://github.com/skynbe/FedRCL)] - Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Leak_and_Learn_An_Attackers_Cookbook_to_Train_Using_Leaked_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Leak_and_Learn_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2403.18144)] [[VIDEO](https://www.youtube.com/watch?v=ovmSnjSOcks)] - Traceable Federated Continual Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Traceable_Federated_Continual_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Traceable_Federated_Continual_CVPR_2024_supplemental.pdf)] [[CODE](https://github.com/POwerWeirdo/TagFCL)] - Federated Online Adaptation for Deep Stereo. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Poggi_Federated_Online_Adaptation_for_Deep_Stereo_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Poggi_Federated_Online_Adaptation_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2405.14873)] [[CODE](https://github.com/mattpoggi/fedstereo)] [[PAGE](https://fedstereo.github.io/)] [[VIDEO](https://youtu.be/gVpWsjrUTJc)] - Federated Generalized Category Discovery. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Pu_Federated_Generalized_Category_Discovery_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Pu_Federated_Generalized_Category_CVPR_2024_supplemental.zip)] [[PDF](https://arxiv.org/abs/2305.14107)] [[CODE](https://github.com/TPCD/FedGCD)] - Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Le_Efficiently_Assemble_Normalization_Layers_and_Regularization_for_Federated_Domain_Generalization_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Le_Efficiently_Assemble_Normalization_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2403.15605)] [[CODE](https://github.com/lhkhiem28/gPerXAN)] - Text-Enhanced Data-free Approach for Federated Class-Incremental Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Tran_Text-Enhanced_Data-free_Approach_for_Federated_Class-Incremental_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tran_Text-Enhanced_Data-free_Approach_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2403.14101)] [[CODE](https://github.com/tmtuan1307/lander)] - PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Xie_PerAda_Parameter-Efficient_Federated_Learning_Personalization_with_Generalization_Guarantees_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_PerAda_Parameter-Efficient_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2302.06637)] [[CODE](https://github.com/NVlabs/PerAda)] - FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Lee_FedSOL_Stabilized_Orthogonal_Learning_with_Proximal_Restrictions_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lee_FedSOL_Stabilized_Orthogonal_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2308.12532)] [[CODE](https://github.com/Lee-Gihun/FedSOL)] - FedUV: Uniformity and Variance for Heterogeneous Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Son_FedUV_Uniformity_and_Variance_for_Heterogeneous_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Son_FedUV_Uniformity_and_CVPR_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2402.18372)] - FedAS: Bridging Inconsistency in Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Yang_FedAS_Bridging_Inconsistency_in_Personalized_Federated_Learning_CVPR_2024_paper.html)] [[CODE](https://github.com/xiyuanyang45/FedAS)] - FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Tamirisa_FedSelect_Personalized_Federated_Learning_with_Customized_Selection_of_Parameters_for_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tamirisa_FedSelect_Personalized_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2404.02478)] [[CODE](https://github.com/lapisrocks/fedselect)] - Device-Wise Federated Network Pruning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Device-Wise_Federated_Network_Pruning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gao_Device-Wise_Federated_Network_CVPR_2024_supplemental.pdf)] - Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Byzantine-robust_Decentralized_Federated_Learning_via_Dual-domain_Clustering_and_Trust_Bootstrapping_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_Byzantine-robust_Decentralized_Federated_CVPR_2024_supplemental.pdf)] - DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Bai_DiPrompT_Disentangled_Prompt_Tuning_for_Multiple_Latent_Domain_Generalization_in_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bai_DiPrompT_Disentangled_Prompt_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2403.08506)] - An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_An_Upload-Efficient_Scheme_for_Transferring_Knowledge_From_a_Server-Side_Pre-trained_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_An_Upload-Efficient_Scheme_CVPR_2024_supplemental.zip)] [[PDF](https://arxiv.org/abs/2403.15760)] [[CODE](https://github.com/tsingz0/fedktl)] [[POSTER](https://github.com/TsingZ0/FedKTL/blob/main/FedKTL.png)] [[SLIDES](https://github.com/TsingZ0/FedKTL/blob/main/FedKTL.pdf)] - An Aggregation-Free Federated Learning for Tackling Data Heterogeneity. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Wang_An_Aggregation-Free_Federated_Learning_for_Tackling_Data_Heterogeneity_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_An_Aggregation-Free_Federated_CVPR_2024_supplemental.pdf)] [[PDF](https://arxiv.org/abs/2404.18962)] - FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_FLHetBench_Benchmarking_Device_and_State_Heterogeneity_in_Federated_Learning_CVPR_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_FLHetBench_Benchmarking_Device_CVPR_2024_supplemental.pdf)] [[CODE](https://github.com/Carkham/FLHetBench)] [[PAGE](https://carkham.github.io/FL_Het_Bench/)] [[POSTER](https://drive.google.com/file/d/1Ln0cnptSn5EfML6ughQ7NowwjjLfMYgu/view?usp=sharing)] [[VIDEO](https://www.youtube.com/watch?v=zDGPt3929l8)] - Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01164)] - Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space. [[PUB](https://doi.org/10.1109/CVPR52733.2024.02302)] - CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01028)] [[CODE](https://github.com/Nicholas0228/Revelio)] - Cloud-Device Collaborative Learning for Multimodal Large Language Models. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01202)] - DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models. [[PUB](https://doi.org/10.1109/CVPR52733.2024.02598)] - Dual-Enhanced Coreset Selection with Class-Wise Collaboration for Online Blurry Class Incremental Learning. [[PUB](https://doi.org/10.1109/CVPR52733.2024.02265)] - Improving Plasticity in Online Continual Learning via Collaborative Learning. [[PUB](https://doi.org/10.1109/CVPR52733.2024.02214)] [[CODE](https://github.com/maorong-wang/CCL-DC)] - NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging. [[PUB](https://doi.org/10.1109/CVPR52733.2024.00852)] [[CODE](https://github.com/univ-esuty/noisecollage)] - Shallow-Deep Collaborative Learning for Unsupervised Visible-Infrared Person Re-Identification. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01596)] - Training-Free Pretrained Model Merging. [[PUB](https://doi.org/10.1109/CVPR52733.2024.00565)] [[CODE](https://github.com/zju-vipa/training_free_model_merging)] - Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline. [[PUB](https://doi.org/10.1109/CVPR52733.2024.01180)] #### CVPR workshop - Collaborative Visual Place Recognition through Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/html/Dutto_Collaborative_Visual_Place_Recognition_through_Federated_Learning_CVPRW_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/supplemental/Dutto_Collaborative_Visual_Place_CVPRW_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2404.13324)] - FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer. [[PUB](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/html/Gao_FedProK_Trustworthy_Federated_Class-Incremental_Learning_via_Prototypical_Feature_Knowledge_Transfer_CVPRW_2024_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/supplemental/Gao_FedProK_Trustworthy_Federated_CVPRW_2024_supplemental.pdf)] [[PDF](http://arxiv.org/abs/2405.02685)] - Federated Hyperparameter Optimization Through Reward-Based Strategies: Challenges and Insights. [[PUB](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/html/Nakka_Federated_Hyperparameter_Optimization_Through_Reward-Based_Strategies_Challenges_and_Insights_CVPRW_2024_paper.html)] - On the Efficiency of Privacy Attacks in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2024W/FedVision-2024/html/Tabassum_On_the_Efficiency_of_Privacy_Attacks_in_Federated_Learning_CVPRW_2024_paper.html)] [[PDF](http://arxiv.org/abs/2404.09430)] ### 2023 #### ijcv - AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis. [[PUB](https://doi.org/10.1007/s11263-023-01791-0)] #### MM - FedCE: Personalized Federated Learning Method based on Clustering Ensembles. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612217)] - FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611958)] - Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612597)] [[PDF](https://arxiv.org/abs/2312.02416)] [[CODE](https://github.com/J1nqianChen/FedKA)] - Federated Deep Multi-View Clustering with Global Self-Supervision. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612027)] [[PDF](https://arxiv.org/abs/2309.13697)] - FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611953)] - Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3613837)] [[CODE](https://github.com/exquisite1210/PT-FUCH_P)] - Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612178)] [[PDF](https://arxiv.org/abs/2308.11646)] - FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611966)] - Federated Learning with Label-Masking Distillation. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611984)] [[CODE](https://github.com/wnma3mz/FedLMD)] - Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612481)] [[PDF](https://arxiv.org/abs/2308.03457)] [[CODE](https://github.com/qizhuang-qz/FedCSPC)] - A Four-Pronged Defense Against Byzantine Attacks in Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612474)] [[PDF](https://arxiv.org/abs/2308.03331)] - Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611757)] - FedGH: Heterogeneous Federated Learning with Generalized Global Header. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3611781)] [[PDF](https://arxiv.org/abs/2303.13137)] [[CODE](https://github.com/LipingYi/FedGH)] - Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612134)] [[PDF](https://arxiv.org/abs/2308.03432)] [[CODE](https://github.com/yuxuanzhang0713/fedcsr)] - AffectFAL: Federated Active Affective Computing with Non-IID Data. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612442)] [[CODE](https://github.com/AffectFAL/AffectFAL)] - Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation. [[PUB](https://dl.acm.org/doi/10.1145/3581783.3612350)] - Collaborative Learning of Diverse Experts for Source-free Universal Domain Adaptation. [[PUB](https://doi.org/10.1145/3581783.3612211)] - Gradient-Free Textual Inversion. [[PUB](https://doi.org/10.1145/3581783.3612599)] - Practical Edge Detection via Robust Collaborative Learning. [[PUB](https://doi.org/10.1145/3581783.3612099)] - Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement. [[PUB](https://doi.org/10.1145/3581783.3612077)] #### ICCV - Towards Attack-tolerant Federated Learning via Critical Parameter Analysis. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Han_Towards_Attack-tolerant_Federated_Learning_via_Critical_Parameter_Analysis_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2308.09318)] [[CODE](https://github.com/Sungwon-Han/FEDCPA)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Han_Towards_Attack-tolerant_Federated_ICCV_2023_supplemental.pdf)] - Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Yang_Efficient_Model_Personalization_in_Federated_Learning_via_Client-Specific_Prompt_Generation_ICCV_2023_paper.html)] [[PDF](https://arxiv.org/abs/2308.15367)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Yang_Efficient_Model_Personalization_ICCV_2023_supplemental.pdf)] - Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_Generative_Gradient_Inversion_via_Over-Parameterized_Networks_in_Federated_Learning_ICCV_2023_paper.html)] [[CODE](https://github.com/czhang024/CI-Net)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zhang_Generative_Gradient_Inversion_ICCV_2023_supplemental.pdf)] - GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_GPFL_Simultaneously_Learning_Global_and_Personalized_Feature_Information_for_Personalized_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2308.10279)] [[CODE](https://github.com/TsingZ0/GPFL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zhang_GPFL_Simultaneously_Learning_ICCV_2023_supplemental.zip)] - Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Chen_Workie-Talkie_Accelerating_Federated_Learning_by_Overlapping_Computing_and_Communications_via_ICCV_2023_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Chen_Workie-Talkie_Accelerating_Federated_ICCV_2023_supplemental.pdf)] - PGFed: Personalize Each Client's Global Objective for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Luo_PGFed_Personalize_Each_Clients_Global_Objective_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](https://arxiv.org/abs/2212.01448)] [[CODE](https://github.com/ljaiverson/pgfed)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Luo_PGFed_Personalize_Each_ICCV_2023_supplemental.pdf)] - FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Sun_FedPerfix_Towards_Partial_Model_Personalization_of_Vision_Transformers_in_Federated_ICCV_2023_paper.html)] [[PDF](https://arxiv.org/abs/2308.09160)] [[CODE](https://github.com/imguangyu/fedperfix)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Sun_FedPerfix_Towards_Partial_ICCV_2023_supplemental.pdf)] - L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Rehman_L-DAWA_Layer-wise_Divergence_Aware_Weight_Aggregation_in_Federated_Self-Supervised_Visual_ICCV_2023_paper.html)] [[PDF](https://arxiv.org/abs/2307.07393)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Rehman_L-DAWA_Layer-wise_Divergence_ICCV_2023_supplemental.pdf)] - FedPD: Federated Open Set Recognition with Parameter Disentanglement. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Yang_FedPD_Federated_Open_Set_Recognition_with_Parameter_Disentanglement_ICCV_2023_paper.html)] [[CODE](https://github.com/CityU-AIM-Group/FedPD)] - TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_TARGET_Federated_Class-Continual_Learning_via_Exemplar-Free_Distillation_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.06937)] [[CODE](https://github.com/zj-jayzhang/Federated-Class-Continual-Learning)] - Towards Instance-adaptive Inference for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Feng_Towards_Instance-adaptive_Inference_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2308.06051)] [[CODE](https://github.com/chunmeifeng/fedins)] - Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhou_Communication-efficient_Federated_Learning_with_Single-Step_Synthetic_Features_Compressor_for_Faster_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2302.13562)] [[CODE](https://github.com/Soptq/iccv23-3sfc)] - zPROBE: Zero Peek Robustness Checks for Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Ghodsi_zPROBE_Zero_Peek_Robustness_Checks_for_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2206.12100)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Ghodsi_zPROBE_Zero_Peek_ICCV_2023_supplemental.pdf)] - ProtoFL: Unsupervised Federated Learning via Prototypical Distillation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Kim_ProtoFL_Unsupervised_Federated_Learning_via_Prototypical_Distillation_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2307.12450)] - MAS: Towards Resource-Efficient Federated Multiple-Task Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zhuang_MAS_Towards_Resource-Efficient_Federated_Multiple-Task_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2307.11285)] [[CODE](https://github.com/EasyFL-AI/EasyFL/tree/master/applications/mas)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zhuang_MAS_Towards_Resource-Efficient_ICCV_2023_supplemental.pdf)] - FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Guo_FSAR_Federated_Skeleton-based_Action_Recognition_with_Adaptive_Topology_Structure_and_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2306.11046)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Guo_FSAR_Federated_Skeleton-based_ICCV_2023_supplemental.pdf)] - When Do Curricula Work in Federated Learning?. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Vahidian_When_Do_Curricula_Work_in_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2212.12712)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Vahidian_When_Do_Curricula_ICCV_2023_supplemental.pdf)] - Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Sun_Communication-Efficient_Vertical_Federated_Learning_with_Limited_Overlapping_Samples_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.16270)] [[CODE](https://github.com/NVIDIA/NVFlare/tree/main/research/one-shot-vfl)] - Multi-Metrics Adaptively Identifies Backdoors in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Huang_Multi-Metrics_Adaptively_Identifies_Backdoors_in_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.06601)] [[CODE](https://github.com/siquanhuang/Multi-metrics_against_backdoors_in_FL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Huang_Multi-Metrics_Adaptively_Identifies_ICCV_2023_supplemental.pdf)] - No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Li_No_Fear_of_Classifier_Biases_Neural_Collapse_Inspired_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.10058)] [[CODE](https://github.com/zexilee/iccv-2023-fedetf)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Li_No_Fear_of_ICCV_2023_supplemental.pdf)] - FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Chen_FRAug_Tackling_Federated_Learning_with_Non-IID_Features_via_Representation_Augmentation_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2205.14900)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Chen_FRAug_Tackling_Federated_ICCV_2023_supplemental.pdf)] - Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Wu_Bold_but_Cautious_Unlocking_the_Potential_of_Personalized_Federated_Learning_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2309.11103)] [[CODE](https://github.com/kxzxvbk/Fling)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Wu_Bold_but_Cautious_ICCV_2023_supplemental.pdf)] - Global Balanced Experts for Federated Long-Tailed Learning. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Zeng_Global_Balanced_Experts_for_Federated_Long-Tailed_Learning_ICCV_2023_paper.html)] [[CODE](https://github.com/Spinozaaa/Federated-Long-tailed-Learning)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Zeng_Global_Balanced_Experts_ICCV_2023_supplemental.pdf)] - Knowledge-Aware Federated Active Learning with Non-IID Data. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Cao_Knowledge-Aware_Federated_Active_Learning_with_Non-IID_Data_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2211.13579)] [[CODE](https://github.com/ycao5602/KAFAL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Cao_Knowledge-Aware_Federated_Active_ICCV_2023_supplemental.pdf)] - Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Wan_Enhancing_Privacy_Preservation_in_Federated_Learning_via_Learning_Rate_Perturbation_ICCV_2023_paper.html)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Wan_Enhancing_Privacy_Preservation_ICCV_2023_supplemental.pdf)] - Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Cho_Local_or_Global_Selective_Knowledge_Assimilation_for_Federated_Learning_with_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2307.08809)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Cho_Local_or_Global_ICCV_2023_supplemental.pdf)] - Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Hu_Federated_Learning_Over_Images_Vertical_Decompositions_and_Pre-Trained_Backbones_Are_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2309.03237)] [[CODE](https://github.com/huerdong/FedVert-Experiments)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Hu_Federated_Learning_Over_ICCV_2023_supplemental.pdf)] - Robust Heterogeneous Federated Learning under Data Corruption. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Fang_Robust_Heterogeneous_Federated_Learning_under_Data_Corruption_ICCV_2023_paper.html)] [[CODE](https://github.com/FangXiuwen/AugHFL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Fang_Robust_Heterogeneous_Federated_ICCV_2023_supplemental.pdf)] - Personalized Semantics Excitation for Federated Image Classification. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Xia_Personalized_Semantics_Excitation_for_Federated_Image_Classification_ICCV_2023_paper.html)] [[CODE](https://github.com/HaifengXia/PSE)] - Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology. [[PUB](https://openaccess.thecvf.com/content/ICCV2023/html/Do_Reducing_Training_Time_in_Cross-Silo_Federated_Learning_Using_Multigraph_Topology_ICCV_2023_paper.html)] [[PDF](http://arxiv.org/abs/2207.09657)] [[CODE](https://github.com/aioz-ai/MultigraphFL)] [[SUPP](https://openaccess.thecvf.com/content/ICCV2023/supplemental/Do_Reducing_Training_Time_in_Cross-Silo_Federated_Learning_Using_Multigraph_Topology_ICCV_2023_supplemental.pdf)] - Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning. [[PUB](https://doi.org/10.1109/ICCVW60793.2023.00362)] - FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data. [[PUB](https://doi.org/10.1109/ICCVW60793.2023.00111)] - FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning. [[PUB](https://doi.org/10.1109/ICCVW60793.2023.00371)] - PGFed: Personalize Each Client's Global Objective for Federated Learning. [[PUB](https://doi.org/10.1109/ICCV51070.2023.00365)] [[CODE](https://github.com/ljaiverson/pgfed)] - Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning. [[PUB](https://doi.org/10.1109/ICCVW60793.2023.00240)] - A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation. [[PUB](https://doi.org/10.1109/ICCV51070.2023.01076)] - Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking. [[PUB](https://doi.org/10.1109/ICCV51070.2023.00914)] [[CODE](https://github.com/yolomax/ColTrack)] - GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization. [[PUB](https://doi.org/10.1109/ICCV51070.2023.00458)] - HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning. [[PUB](https://doi.org/10.1109/ICCV51070.2023.01898)] - Quality-Agnostic Deepfake Detection with Intra-model Collaborative Learning. [[PUB](https://doi.org/10.1109/ICCV51070.2023.02045)] #### ICCV workshop - Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10350693)] [[PDF](https://arxiv.org/abs/2310.01366)] - Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning. [[PUB](https://ieeexplore.ieee.org/document/10350429)] - FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning. [[PUB](https://ieeexplore.ieee.org/document/10350898)] [[CODE](https://github.com/chatzikon/FedRCIL)] - FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data. [[PUB](https://ieeexplore.ieee.org/document/10350371)] #### CVPR - Rethinking Federated Learning With Domain Shift: A Prototype View. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Rethinking_Federated_Learning_With_Domain_Shift_A_Prototype_View_CVPR_2023_paper.html)] [[CODE](https://github.com/WenkeHuang/RethinkFL)] - Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Li_Class_Balanced_Adaptive_Pseudo_Labeling_for_Federated_Semi-Supervised_Learning_CVPR_2023_paper.html)] [[CODE](https://github.com/minglllli/CBAFed)] - DaFKD: Domain-Aware Federated Knowledge Distillation. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.html)] [[CODE](https://github.com/haozhaowang/DaFKD2023)] - The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.14868)] - FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Miao_FedSeg_Class-Heterogeneous_Federated_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html)] - On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Li_On_the_Effectiveness_of_Partial_Variance_Reduction_in_Federated_Learning_CVPR_2023_paper.html)] [[PDF](https://arxiv.org/abs/2212.02191)] - Elastic Aggregation for Federated Optimization. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Elastic_Aggregation_for_Federated_Optimization_CVPR_2023_paper.html)] - FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_FedDM_Iterative_Distribution_Matching_for_Communication-Efficient_Federated_Learning_CVPR_2023_paper.html)] [[PDF](https://arxiv.org/abs/2207.09653)] - Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Liao_Adaptive_Channel_Sparsity_for_Federated_Learning_Under_System_Heterogeneity_CVPR_2023_paper.html)] - ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Ilhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.html)] [[CODE](https://github.com/git-disl/scale-fl)] - Reliable and Interpretable Personalized Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.html)] - Federated Domain Generalization With Generalization Adjustment. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Federated_Domain_Generalization_With_Generalization_Adjustment_CVPR_2023_paper.html)] [[CODE](https://github.com/MediaBrain-SJTU/FedDG-GA)] - Make Landscape Flatter in Differentially Private Federated Learning. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Make_Landscape_Flatter_in_Differentially_Private_Federated_Learning_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.11242)] [[CODE](https://github.com/YMJS-Irfan/DP-FedSAM)] - Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.html)] [[PDF](https://arxiv.org/abs/2305.12557)] [[CODE](https://github.com/junyizhu-ai/confidence_aware_pfl)] - STDLens: Model Hijacking-Resilient Federated Learning for Object Detection. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Chow_STDLens_Model_Hijacking-Resilient_Federated_Learning_for_Object_Detection_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.11511)] [[CODE](https://github.com/git-disl/STDLens)] - Re-Thinking Federated Active Learning Based on Inter-Class Diversity. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Re-Thinking_Federated_Active_Learning_Based_on_Inter-Class_Diversity_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.12317)] [[CODE](https://github.com/raymin0223/LoGo)] - Learning Federated Visual Prompt in Null Space for MRI Reconstruction. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Learning_Federated_Visual_Prompt_in_Null_Space_for_MRI_Reconstruction_CVPR_2023_paper.html)] [[PDF](http://arxiv.org/abs/2303.16181)] [[CODE](https://github.com/chunmeifeng/FedPR)] - Fair Federated Medical Image Segmentation via Client Contribution Estimation. [[PUB](https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Fair_Federated_Medical_Image_Segmen

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