FedDAB用两阶段方法检测恶意更新，比CRFL和FLTrust更有效，理论收敛有保证。
FedDAB是一种两阶段防御方法，通过局部对比正则化和对齐检查来抵御联邦学习中的后门攻击。第一阶段在局部目标中添加模型对比项，增强良性更新的方向和幅度一致性；第二阶段利用整体方向对齐和参数级对齐检查历史信息，排除异常更新。理论证明收敛率为O(1/T)，实验表明FedDAB在多个基准上优于现有防御方法如CRFL和FLTrust。
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 strategy to evaluate each local update in terms of overall-direction alignment and parameter-level alignment with historical information, excluding updates that exhibit abnormal alignment patterns from global aggregation. We theoretically prove FedDAB's robustness with a convergence rate of $\mathcal{O}(1/T)$. Extensive experiments show that FedDAB outperforms existing defense methods against backdoor attacks.