Zhou Tan — accepted AI-conference papers

2026

DoBlock: Blocking Malicious Association Propagation for Backdoor-Robust Federated Learning Under Domain Skew

Zhou Tan, De Li, Yirui Huang, Duanshu Fang, Jia-Li Yin, Xiaolei Liu +2

AAAI 2026technical

Federated Learning (FL) enables privacy-preserving distributed training but remains vulnerable to backdoor attacks. Attackers can embed malicious trigger-label associations into the global model by participating in the aggregation process. Existing defense methods typically defend against backdoor a

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2026

FedRGL: Robust Federated Graph Learning under Label Noise

De Li, Zhou Tan, Qiyu Li, Zeming Gan, Tiange Xia, Chunpei Li +1

ICML 2026poster

Federated Graph Learning (FGL) is a distributed machine learning paradigm based on graph neural networks, enabling secure and collaborative modeling of local graph data among clients. However, label noise in graph data can degrade the generalization performance of the global model. Existing federate…

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