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

Summary

Fed-CBE is a client-side defense for federated learning that removes backdoors through three mechanisms: periodic alternating layer resetting, indiscriminate forgetting via entropy maximization on non-ground-truth classes, and knowledge distillation with historical local models. Evaluated on three benchmark datasets and model architectures, it keeps attack success rates near zero in most settings, including under high malicious-client ratios, while preserving primary task accuracy.

Mitigation

Client-side defense: apply Fed-CBE, which combines periodic alternating layer resetting, indiscriminate forgetting, and knowledge distillation with historical local models to eliminate backdoors without prior trigger knowledge.

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