Zhuqing Liu

Previous Scheduled Teaching

Published Intellectual Contributions

Conference Proceeding

• Qin, Z., Liu, Z., Lu, S., Liang, Y., Liu, J. (2025). DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity. The Thirteenth International Conference on Learning Representations.
• Liu, Z., Zhang, X., Liu, J., Zhu, Z., Lu, S. (2024). PILOT: An O (1/K)-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation. International Conference on Learning Representations.
• Dou, Z., Hu, X., Yang, H., Liu, Z., Fang, M. (2024). Adversarial Attacks to Multi-Modal Models. LAMPS 2024 - Proceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis. 35-46. https://api.elsevier.com/content/abstract/scopus_id/85215502541
• Qiu, P., Li, Y., Liu, Z., Khanduri, P., Liu, J., Shroff, N.B., Bentley, E.S., Turck, K. (2023). Diamond: Taming sample and communication complexities in decentralized bilevel optimization. IEEE INFOCOM 2023-IEEE conference on computer communications. 1--10.
• Liu, Z., Zhang, X., Lu, S., Liu, J. (2023). Precision: Decentralized constrained min-max learning with low communication and sample complexities. Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. 191--200.
• Liu, Z., Zhang, X., Khanduri, P., Lu, S., Liu, J. (2023). Prometheus: taming sample and communication complexities in constrained decentralized stochastic bilevel learning. International Conference on Machine Learning. 22420--22453.
• Liu, Z., Zhang, X., Khanduri, P., Lu, S., Liu, J. (2022). Interact: Achieving low sample and communication complexities in decentralized bilevel learning over networks. Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. 61--70.
• Zhang, X., Fang, M., Liu, Z., Yang, H., Liu, J., Zhu, Z. (2022). Net-fleet: Achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data. Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. 71--80.
• Liu, Z., Zhang, X., Liu, J. (2022). Synthesis: A semi-asynchronous path-integrated stochastic gradient method for distributed learning in computing clusters. Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. 151--160.
• Zhang, X., Liu, Z., Liu, J., Zhu, Z., Lu, S., Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (2021). Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning. Advances in Neural Information Processing Systems. 34 18825--18838. Curran Associates, Inc.. https://proceedings.neurips.cc/paper_files/paper/2021/file/9c51a13764ca629f439f6accbb4ec413-Paper.pdf
• Liu, Z., Duan, H., Yang, Y., Hu, X. (2016). Pendulum-like oscillation controller for UAV based on L\'evy-flight pigeon-inspired optimization and LQR. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 1--6.

Journal Article

• Fang, M., Liu, Z., Zhao, X., Liu, J. (2025). Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing. Proceedings of the ACM Web Conference 2025.
• Fang, M., Nabavirazavi, S., Liu, Z., Sun, W., Iyengar, S.S., Yang, H. (2025). Do We Really Need to Design New Byzantine-robust Aggregation Rules?. Proceedings Network and Distributed System Security Symposium. https://www.ndss-symposium.org/wp-content/uploads/2025-1796-paper.pdf
• Wang, W., Ma, Q., Zhang, Z., Liu, Y., Liu, Z., Fang, M. (2025). Poisoning Attacks and Defenses to Federated Unlearning. Proceedings of the ACM Web Conference 2025.
• Yang, H., Liu, Z., Liu, J., Dong, C., Momma, M. (2023). Federated multi-objective learning. Other. 36 39602--39625.
• Yang, H., Liu, Z., Zhang, X., Liu, J. (2022). SAGDA: Achieving $$\backslash$mathcal $\$O$\$($\backslash$epsilon\^{}$\$-2$\$) $ Communication Complexity in Federated Min-Max Learning. Other. 35 7142--7154.
• Zhang, X., Liu, Z., Liu, J., Zhu, Z., Lu, S. (2021). Taming communication and sample complexities in decentralized policy evaluation for cooperative multi-agent reinforcement learning. Other. 34 18825--18838.
• Bi, L., Zuo, Y., Tao, F., Liao, T.W., Liu, Z. (2017). Energy-aware material selection for product with multicomponent under cloud environment. Other. 17 (3) 031007. American Society of Mechanical Engineers.