EndPCA: Ensemble Defense With Provably Convergent Aggregation Against Poisoning Attacks in Federated Learning

Despite its success in many applications, federated learning is increasingly vulnerable to sophisticated poisoning attacks. Existing defenses, particularly Byzantine Robust Aggregation Rules (BRARs), offer some protection but rely on strong assumptions or challenging technical prerequisites. To address these shortcomings, we propose anensemble defense with provably convergent aggregation(EndPCA). By using the entropy weight method to consolidate scores from multiple BRARs into an ensemble trust score, it effectively integrates heterogeneous weak BRARs to resist a wide range of poisoning attacks under practical assumptions. We formally prove that EndPCA can provide theoretical guarantees of convergence with bounded error. Our empirical evaluations show that EndPCA consistently outperforms existing BRARs, demonstrating its effectiveness across various scenarios.