Toward Model-Contrastive Federated Learning With Lightweight Privacy Preservation and Poisoning Attack Detection

Federated learning (FL), a distributed computing paradigm, is vulnerable to poisoning attacks that impair model performance and privacy attacks that leak participant information. Existing FL defense schemes struggle to counter poisoning attacks under data heterogeneity and high privacy computation overhead, limiting the practicality of federated learning. To address these issues, this paper proposes a model-contrastive federated learning framework with lightweight privacy preservation and poisoning attack detection, named MCFL. Specifically, we design a novel model-contrastive term by aligning intermediate-layer representations of models in the local optimization function to promote consistency of model updates among benign participants. Additionally, we design a secure aggregation protocol that adopts two-server aggregation instead of the single server to resist poisoning attacks with lightweight privacy protection. The proposed MCFL is theoretically proven in terms of convergence, robustness, and privacy. Extensive experiments demonstrate the superiority of MCFL compared to existing FL defense schemes.