FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
Federated Learning remains highly susceptible to backdoor attacks--malicious clients inject targeted behaviours into the global model. Existing defenses suffer from substantial false-positive rates under realistic non-independent and identically distributed (non-IID) data, incorrectly flagging benign clients and degrading model accuracy even when adversaries are correctly identified. We present FedSurrogate, a novel backdoor defense that addresses this limitation by combining bidirectional gradient alignment filtering with layer-adaptive anomaly detection. FedSurrogate performs selective clustering on security-critical layers identified via directional divergence analysis, concentrating t...
| Authors | Fatima Z. Abacha, Sin G. Teo, Yuanxiang Wu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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