CFL: Cluster Federated Learning in Large-scale Peer-to-Peer Networks

Understand

Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' local devices.

• However, the parameter server setting of FL not only has high bandwidth requirements, but also poses data privacy issues and a single point of failure.
• In this paper, we propose an efficient and privacy-preserving protocol, dubbed CFL, which is the first fine-grained global model training for FL in large-scale peer-to-peer (P2P) networks.
• Unlike previous FL in P2P networks, CFL aggregates local model update parameters hierarchically, which improves the communication efficiency facing large amounts of clients.