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