Dr. Kun Gao
Assistant Professor Master Supervisor
Email: kungao@cityu.edu.mo
Academic Qualifications
2026 Doctor of Philosophy (PhD)，Computer Science / Information Systems / Software Engineering and Data Analytics, University of Technology Sydney (UTS),Australia
2022 Bachelor of Information Technology (Honours),University of Technology Sydney (UTS), Australia
2018 Bachelor of Engineering in Computer Science and TechnologyChina University of Geosciences (Wuhan), China
Research Interests
Federated Learning
Machine Unlearning
Reinforcement Learning
Privacy-Preserving Machine Learning
Large Language Model (LLM) Security.
Research & Publications
1. Gao, K., Zhu, T., Ye, D., Liu, B., & Zhou, W. (2026). Hidden threats in federated unlearning: Camouflaged poisoning attacks and their unlearning consequences. IEEE Transactions on Dependable and Secure Computing, 23(2), 2934–2948. https://doi.org/10.1109/TDSC.2025.3630811
2. Gao, K., Zhu, T., Ye, D., Gao, L., & Zhou, W. (2025). Federated unlearning with reinforcement learning: Adaptive privacy preservation for clients. Journal of Information Security and Applications, 93, 104164. https://doi.org/10.1016/j.jisa.2025.104164
3. Gao, K., Zhu, T., Ye, D., & Zhou, W. (2024). Defending against gradient inversion attacks in federated learning via statistical machine unlearning. Knowledge-Based Systems, 299, 111983. https://doi.org/10.1016/j.knosys.2024.111983
4. Ye, D., Zhu, T., Zhu, C., Wang, D., Gao, K., Shi, Z., Shen, S., Zhou, W., & Xue, M. (2025). Reinforcement unlearning. In Proceedings of the Network and Distributed System Security Symposium (NDSS 2025). https://doi.org/10.14722/ndss.2025.230080
5. Ye, D., Zhu, T., Gao, K., & Zhou, W. (2024). Defending against label-only attacks via meta-reinforcement learning. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2024.3357292
6. Ye, D., Zhu, T., Gao, K., Zhu, C., & Zhou, W. (2025). Cooperating or kicking out: Defending against poisoning attacks in federated learning via the evolution of cooperation. IEEE Transactions on Dependable and Secure Computing. https://doi.org/10.1109/TDSC.2025.3532351
7. Zhang, T., Zhu, T., Gao, K., Zhou, W., & Yu, P. S. (2021). Balancing learning model privacy, fairness, and accuracy with early stopping criteria. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3129592
8. Ye, D., Zhu, T., Li, J., Gao, K., Liu, B., Zhang, L. Y., Zhou, W., & Zhang, Y. (2025). Data duplication: A novel multi-purpose attack paradigm in machine unlearning. In 34th USENIX Security Symposium (USENIX Security 25) (pp. 6399–6418). USENIX Association.
