README.md

metadata

title: Federated Credit Scoring
emoji: 🚀
colorFrom: red
colorTo: red
sdk: streamlit
app_port: 8501
tags:
  - streamlit
  - federated-learning
  - machine-learning
  - privacy
pinned: false
short_description: Federated Learning Credit Scoring Demo with Privacy-Preserving Model Training
license: mit

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
• 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
• NEW: Interactive Web Demo - Try it out without setup!

Quick Demo (No Installation Required)

🚀 Live Demo: Hugging Face Spaces

The web demo allows you to:

• Enter customer features and get credit score predictions
• See how federated learning works
• Understand privacy-preserving ML concepts

Installation

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

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
• Toggle between Demo Mode (no server required) and Real Mode (connects to server)

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

Project Structure

FinFedRAG-Financial-Federated-RAG/
├── src/
│   ├── api/           # REST API for server and client communication
│   ├── client/        # Federated learning client implementation
│   ├── server/        # Federated learning server and coordinator
│   ├── rag/           # Retrieval-Augmented Generation components
│   ├── models/        # VAE/GAN models for data generation
│   └── utils/         # Privacy, metrics, and utility functions
├── webapp/            # Streamlit web application
├── config/            # Configuration files
├── tests/             # Unit and integration tests
├── docker/            # Docker configurations
├── kubernetes/        # Kubernetes deployment files
└── app.py             # Root app.py for Hugging Face Spaces deployment

License

MIT

Contributing

Please read our contributing guidelines before submitting pull requests.

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Demo URL: https://huggingface.co/spaces/ArchCoder/federated-credit-scoring