Towards Privacy-Enhanced and Robust Clustered Federated Learning

Clustered federated learning (CFL) leverages data distribution similarities to cluster clients, facilitating personalized model training under data heterogeneity. However, most existing CFL schemes pose potential privacy risks for clients (e.g., gradient inversion attacks) as they rely on individual gradients for clustering. This also renders them incompatible with secure aggregation mechanisms that are widely employed in federated learning for privacy protection. Moreover, CFL introduces the risk of malicious clients dominating several clusters and conducting poisoning attacks therein, thereby threatening secure model training. To address these issues, we propose ProCFL, a Privacy-Enhanced and Robust CFL framework incorporating gradient-free clustering and peer validation. Specifically, we first design a new protocol for measuring data distribution similarity among clients without using their gradient information. Then, we transform the client clustering process into a weighted set covering problem and introduce a diversity-optimized clustering algorithm to achieve near-optimal clustering results while eliminating any need for prior knowledge. Furthermore, we develop a post-hoc detection mechanism that employs peer validation to identify and discard malicious client models. Extensive experimental evaluation of ProCFL validates its superior model robustness and accuracy performance compared to existing schemes.