Author

Ji-Guo Yu

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#machine learning Preprint Sep 2026

Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery

Continual learning (CL) enables models to acquire new knowledge from sequentially arriving tasks while retaining previously learned knowledge. However, in practical scenarios, task streams collected from untrusted sources may contain backdoor-poisoned samples, posing a critical challenge to the stability, plasticity, a...

Ke-Yu Lin, Fei Ye, Qi-He Liu et al. · 0 citations

Jul 2026

Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning

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 $\mathcal{O}(1/T)$.

Hongliang Zhang, Zhongyuan Yu, Guijuan Wang et al. · arXiv.org · 0 citations

Preprint Aug 2026

FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices

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.

Hongliang Zhang, Zhongyuan Yu, Fenghua Xu et al. · 0 citations