Guo Chen

Defending Poisoning Attacks in Federated Learning Under System and Data Heterogeneity

Federated learning (FL) is susceptible to poisoning attacks, where malicious clients manipulate local data or models to disrupt training. The system and data heterogeneity inherent in practical FL systems exacerbates these vulnerabilities, rendering existing defense mechanisms ineffective or infeasible. Specifically, d...