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| 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](https://huggingface.co/spaces/ArchCoder/federated-credit-scoring) | |
| 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 | |
| ```bash | |
| # 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** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 2. **Start the Federated Server** | |
| ```bash | |
| python -m src.main --mode server --config config/server_config.yaml | |
| ``` | |
| 3. **Start at least two Clients (in separate terminals)** | |
| ```bash | |
| python -m src.main --mode client --config config/client_config.yaml | |
| ``` | |
| 4. **Run the Web App** | |
| ```bash | |
| 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. | |
| --- | |
| **Demo URL**: https://huggingface.co/spaces/ArchCoder/federated-credit-scoring | |