HFLMLD: Enhancing Robustness in Hierarchical Federated Learning With Multiple Layer Defenses

Hierarchical federated learning (HFL) has attracted significant attention for its communication efficiency and cost-effectiveness. However, its distributed nature makes it vulnerable to malicious attacks, particularly in large-scale, zero-trust edge networks where monitoring nodes is challenging. Existing defenses for traditional two-layer federated learning are insufficient to address the unique cross-layer collusion attacks possible in HFL. To bridge this gap, we introduce HFLMLD, a robust HFL framework with Multiple Layer Defenses. HFLMLD employs a two-stage hierarchical defense strategy to enhance system resilience. At the edge layer, it combines dimensionality reduction-based detection with a dynamic suspicion score mechanism to identify and neutralize malicious clients. At the cloud layer, a normalized weighted aggregation algorithm is employed to counter threats from compromised edge servers. Our extensive experiments on benchmark datasets show that HFLMLD effectively secures HFL systems against complex, multi-faceted attacks while maintaining high model performance.