About This Architecture

Federated Learning Secure Round Protocol orchestrates encrypted model training across distributed clients and a central server using five sequential phases. The Federated Server derives encryption keys, transmits encrypted models to Federated Clients via AES-GCM, and clients decrypt, train locally, then upload encrypted gradients back to the server. The server decrypts gradients and performs federated averaging to update the global model while maintaining end-to-end encryption throughout the round. Fork this diagram to customize key derivation functions, adjust encryption algorithms, or integrate with your federated learning framework. This architecture demonstrates privacy-preserving collaborative learning where neither server nor clients expose raw data or unencrypted model updates.

People also ask

How does federated learning maintain privacy while training models across distributed clients?

This secure round protocol uses AES-GCM encryption at every stage: the server derives keys and encrypts models before transmission, clients decrypt locally, train, and re-encrypt gradients before upload, and the server decrypts and aggregates without ever accessing raw client data or unencrypted updates.