Fed-CBE: Client-Side Backdoor Elimination in Federated Learning via Persistent Parameter Disruption

Abstract

Federated Learning (FL) inherently preserves privacy but remains highly vulnerable to backdoor attacks due to its open participation architecture. Existing defenses face two fundamental limitations: first, screening-based aggregation strategies prove ineffective against advanced cross-round attacks where adversaries progressively poison model parameters through multi-round collaboration; second, mitigation techniques often cause significant accuracy degradation due to the deep entanglement between backdoor and primary task parameters. To address these challenges, we propose Fed-CBE, a novel client-side defense algorithm that eliminates backdoors through three synergistic mechanisms: 1) periodic alternating layer resetting disrupts deep parameters to dismantle cross-round backdoor accumulation; 2) indiscriminate forgetting employs entropy maximization on non-ground-truth classes to decouple backdoor associations without prior trigger knowledge; and 3) knowledge distillation with historical local models restores primary task performance. Extensive evaluations on three benchmark datasets and model architectures demonstrate that Fed-CBE achieves highly competitive robustness, limiting attack success rates to near-zero levels in most settings and keeping them exceptionally low even under high malicious-client ratios without compromising primary task performance, significantly outperforming existing defenses.