Balancing Learning Model Privacy, Fairness, and Accuracy With Early Stopping Criteria. - Tao Zhang 055; Tianqing Zhu; Kun Gao 006; Wanlei Zhou 001; Philip S. Yu (IEEE Trans. Neural Networks Learn. Syst., 2023)

Defending against gradient inversion attacks in federated learning via statistical machine unlearning. - Kun Gao 006; Tianqing Zhu; Dayong Ye; Wanlei Zhou 001 (Knowl. Based Syst., 2024)

Defending Against Label-Only Attacks via Meta-Reinforcement Learning. - Dayong Ye; Tianqing Zhu; Kun Gao 006; Wanlei Zhou 001 (IEEE Trans. Inf. Forensics Secur., 2024)

Reinforcement Unlearning. - Dayong Ye; Tianqing Zhu; Congcong Zhu; Derui Wang; Kun Gao 006; Zewei Shi; Sheng Shen 005; Wanlei Zhou 001; Minhui Xue 001 (NDSS, 2025)

Federated Unlearning With Reinforcement Learning: Adaptive Privacy Preservation for Clients. - Kun Gao 006; Tianqing Zhu; Dayong Ye; Longxiang Gao; Wanlei Zhou 001 (J. Inf. Secur. Appl., 2025)

Cooperating or Kicking Out: Defending Against Poisoning Attacks in Federated Learning via the Evolution of Cooperation. - Dayong Ye; Tianqing Zhu; Kun Gao 006; Congcong Zhu; Wanlei Zhou 001 (IEEE Trans. Dependable Secur. Comput., 2025)

Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning. - Dayong Ye; Tainqing Zhu; Jiayang Li; Kun Gao 006; Bo Liu 001; Leo Yu Zhang; Wanlei Zhou 001; Yang Zhang (CoRR, 2025)