Robust clustering federated learning with trusted anchor clients

Federated Learning (FL) is a distributed machine learning framework that has attracted widespread attention. However, its decentralized architecture makes it vulnerable to attack with malicious data or model injection. While existing methods are can defend against a limited number of malicious clients, the challenge of defending against model poisoning attacks from a large number of malicious clients remains an unresolved issue. To address these challenges. We propose the Robust Clustering Federated Learning with Trusted Anchor Clients, which aims to provide clean global models for specified trusted client’s enterprise (trusted client as anchor client), even in the presence of a substantial number of malicious clients. Specifically, it performs classification by extracting clustering factors from the differences between anchor clients and other clients. It then identifies trustworthy clusters as aggregation clusters to identify the most likely benign clients. Extensive experiments on two datasets demonstrate that our method maintains robust defense efficacy, even in scenarios involving numerous malicious clients (more than 50%) or highly non-independent, non-identically distributed data.