Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning
Federated Learning (FL) is vulnerable to backdoor attacks because of its distributed nature in edge computing scenarios. Existing defense methods show limited efficacy as they overlook the deviations among benign local updates caused by statistical heterogeneity and the stealthiness of backdoor attacks. To tackle these issues, we propose FedDAB, a two-phase method that combines local contrastive regularization with alignment checking, to defend against backdoor attacks. In the first phase, FedDAB introduces a novel model-contrastive term into the local objective to enhance direction and magnitude consistency among benign updates. In the second phase, FedDAB employs an alignment checking s...
| Authors | Hongliang Zhang, Zhongyuan Yu, Guijuan Wang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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