federated learning framework

built a federated learning framework enabling decentralized model training across multiple clients while preserving data privacy and supporting diverse data environments.

developed a fastapi-based server for client registration, model aggregation, and global weight distribution, integrated websockets for real-time synchronization, and used azure blob storage for efficient weight management with differential privacy applied on client-side training.