# Federated Learning for Privacy-Preserving Financial Data Generation

## Overview
This documentation covers the implementation and usage of a federated learning system for generating synthetic financial data with privacy preservation using RAG (Retrieval-Augmented Generation).

## Quick Start
- [Installation Guide](guides/installation.md)
- [Usage Guide](guides/usage.md)
- [API Reference](api/index.md)
- [Project Planning](guides/planning.md)

## Architecture
The system consists of three main components:
1. Federated Learning Framework
2. Privacy-Preserving Data Generation
3. RAG Integration

## Components
- Client Implementation
- Server Coordination
- RAG System
- Privacy Management
- Data Handling

## Contributing
Please read our [Contributing Guidelines](guides/contributing.md) for details on submitting pull requests.

## License
This project is licensed under the MIT License - see the LICENSE file for details.