README.md

Federated Learning for Privacy-Preserving Financial Data Generation with RAG Integration

This project implements a federated learning framework combined with a Retrieval-Augmented Generation (RAG) system to generate privacy-preserving synthetic financial data.

Features

• Federated Learning using TensorFlow Federated
• Privacy-preserving data generation using VAE/GAN
• RAG integration for enhanced data quality
• Secure Multi-Party Computation (SMPC)
• Differential Privacy implementation
• Kubernetes-based deployment
• Comprehensive monitoring and logging

Installation

pip install -r requirements.txt

Usage

Project Structure

License

MIT

Contributing

Federated Credit Scoring Demo (with Web App)

This project includes a demo where multiple banks (clients) collaboratively train a credit scoring model using federated learning. A Streamlit web app allows you to enter customer features and get a credit score prediction from the federated model.

Quick Start

1. Install dependencies

pip install -r requirements.txt

2. Start the Federated Server

python -m src.main --mode server --config config/server_config.yaml

3. Start at least two Clients (in separate terminals)

python -m src.main --mode client --config config/client_config.yaml

4. Run the Web App

streamlit run webapp/streamlit_app.py

5. Use the Web App

• Enter 32 features (dummy values are fine for demo)
• Click "Predict Credit Score" to get a prediction from the federated model
• View training progress in the app

For best results, keep the server and at least two clients running in parallel.

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