🤖 Federated Learning & Retrieval-Augmented Generation 🚀

Welcome to the Federated Learning (FL) and Retrieval-Augmented Generation (RAG) Notebook!
This repository provides an in-depth look at how to build AI models that preserve privacy while leveraging decentralized data and advanced retrieval techniques.

📚 Overview

Train AI models securely across devices without sharing sensitive data! This project combines FL and RAG to achieve:

• Privacy-Preserving Learning: Train models across decentralized devices without exposing raw data.
• Enhanced AI Performance: Use RAG to fine-tune AI with powerful retrieval methods for smarter outputs.

🏗️ Directory Structure

├── src/             # Source code for FL and RAG components
├── notebooks/       # Jupyter notebooks with examples and tutorials
├── data/            # Sample datasets or data scripts
├── docs/            # Extended documentation
├── tests/           # Unit and integration tests
├── requirements.txt # Python dependencies
├── .gitignore       # Files/folders to ignore in git
├── README.md        # This file
├── LICENSE          # License info

🚀 Installation

git clone https://github.com/ay0788/Federated-Learning-Retrieval-Augmented-Generation-.git
cd Federated-Learning-Retrieval-Augmented-Generation-
pip install -r requirements.txt

📖 Usage

1. Prepare your data (see /data folder or the sample data script).
2. Explore the example notebooks in /notebooks.
3. Use source code in /src to build, train, and evaluate models.
4. Run tests with pytest.

📝 Example

See notebooks/example_fl_rag.ipynb for a full workflow demo!

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

📄 License

This repository is licensed under the MIT License - see LICENSE for details.