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A robust dynamic-expansion framework that integrates sample purification, selective recovery, and robust expert routing into a unified continual learning paradigm is proposed, and Bi-Prototype Purification (BPP) is introduced to identify suspicious samples by exploiting semantic discrepancies in feature space.
FedDAB, a two-phase method that combines local contrastive regularization with alignment checking, to defend against backdoor attacks, is proposed and theoretically proves FedDAB's robustness with a convergence rate of $\mathcal{O}(1/T)$.
In FL-OA, the server collaborates with third-party organization that holds an additional root dataset to perform outsourced auditing, thereby enabling the server to achieve robust aggregation without strong assumptions, demonstrating that FL-OA outperforms existing defense methods against Byzantine attacks.
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