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, FedDA
Record details
Published: 29 July 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Military & security
Retrieved: 30 July 2026
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ethics.ai (29 July 2026), “Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning,” evidence record 14574, https://ethics.ai/record/14574 (originally published by arXiv).
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