Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching

Understand

In federated learning (FL), clients may have diverse objectives, and merging all clients' knowledge into one global model will cause negative transfer to local performance.

• Thus, clustered FL is proposed to group similar clients into clusters and maintain several global models.
• In the literature, centralized clustered FL algorithms require the assumption of the number of clusters and hence are not effective enough to explore the latent relationships among clients.
• In this paper, without assuming the number of clusters, we propose a peer-to-peer (P2P) FL algorithm named PANM.