Mubashir42884/Differential-Privacy-with-PyTorch-Opacus

# Differential Privacy with PyTorch Opacus ## 🗂️ Repository Structure ### The Backbone Skeleton /Differential-Privacy-with-PyTorch-Opacus ├── Tutorials/ # Progressive Opacus learning notebooks │ ├── data/ # Datasets used across the tutorials │ ├── content/ # Attachments, images, and supplementary resources │ ├── documents/ # Cheat sheets, DP-SGD theory, and notes │ ├── 00_notebook.ipynb │ ├── 01_notebook.ipynb │ └── ... # (All the tutorial notebooks) ├── Projects/ # Applied bridging projects │ └── Deep_RL_Mini_Project/ # End-of-term Deep RL project │ ├── data/ │ ├── src/ │ └── notebooks/ ├── Specialization/ # HealCyD framework and Medical AI focus │ ├── HF_Base_Models/ # Workflows for Hugging Face (e.g., Llama-3-8B) │ ├── Private_Finetuning/ # Parameter-efficient techniques (DP-LoRA) │ └── Alignment/ # DP-RLHF and PPO stage implementations ├── requirements.txt # opacus, torch, transformers, etc. ├── README.md # Repository roadmap and objectives └── .gitignore ### 1. Tutorials This directory contains a progressive series of Jupyter Notebooks (`.ipynb`) designed to build foundational to advanced knowledge of Opacus. Each notebook is focused, modular, and designed for a 10-15 minute learning session. * **`data/`**: Datasets used across the tutorials. * **`content/`**: Attachments, images, and supplementary resources for the notebooks. * **`documents/`**: Cheat sheets, theoretical notes on DP-SGD, and official documentation summaries. #### 🗺️ Tutorial Roadmap (In Progress) * `00. Opacus Fundamentals - Concepts and Privacy Engine.ipynb` (✅ Completed) * `01. Opacus Fundamentals - DP-SGD and Hyperparameters.ipynb` (✅ Completed) * `02. Opacus Fundamentals - Model Compatibility and ModuleValidator.ipynb` (✅ Completed) * `03. Opacus in Action - End-to-End Image Classification.ipynb` (✅ Completed) * `04. Opacus Advanced - Handling Memory Bottleneck.ipynb` (✅ Completed) * `05. Opacus Advanced - Custom Layers and Grad Samplers.ipynb` (✅ Completed) * `06. Opacus Advanced - Accounting (RDP vs PRV) and Budgeting.ipynb` (✅ Completed) ### 2. Projects Contains applied projects built during the learning phase, bridging the gap between isolated tutorials and full-scale implementation. This will house the Deep RL mini-project and other intermediate milestones. ### 3. Specialization Dedicated to the intersection of Differential Privacy and Large Language Models, serving as the technical foundation for securing medical AI systems. * **Medical LLMs**: Training and aligning domain-specific models. * **DP-RLHF & PPO Stages**: Applying strict privacy constraints to Reinforcement Learning from Human Feedback. * **Hugging Face Integration**: Workflows for base models like Llama-3-8B. * **Private Fine-Tuning**: Parameter-efficient private fine-tuning techniques (e.g., DP-LoRA).