| # 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 | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## Usage | |
| [Add usage instructions here] | |
| ## Project Structure | |
| [Add project structure description here] | |
| ## License | |
| MIT | |
| ## Contributing | |
| [Add contributing guidelines here] | |