1. Yueyang Quan, Chang Wang, Shengjie Zhai, Minghong Fang, and Zhuqing Liu, "Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach",  in Proc. ACM MobiHoc, Oct. 2025 (acceptance rate: 23%).

2. Zhuqing Liu, Chaosheng Dong, Michinari Momma, Simone Shao, Shaoyuan Xu, Haibo Yang, Jia Liu “STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning” in UAI 2025. (acceptance rate: 31%) 

3. Baolei Zhang, Haoran Xin, Jiatong Li, Dongzhe Zhang, Minghong Fang, Zhuqing Liu, Lihai Nie, and Zheli Liu.  Benchmarking Poisoning Attacks against Retrieval-Augmented Generation. arXiv preprint arXiv:2505.18543 (2025).

4. Zhihao Dou, Jiaqi Wang, Wei Sun, Zhuqing Liu, and Minghong Fang. Toward Malicious Clients Detection in Federated Learning.  In Proc. ACM AsiaCCS, 2025 (acceptance rate: 20.4%).

5. Zhen Qin, Zhuqing Liu, Songtao Lu, Yingbin Liang, and Jia Liu, "DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity," in Proc. ICLR, Singapore, Apr. 2025 (acceptance rate: 32%).

6. Minghong Fang, Seyedsina Nabavirazavi, Zhuqing Liu, Wei Sun, Sundararaja Iyengar, Haibo Yang, "Do We Really Need to Design New Byzantine-robust Aggregation Rules?," in NDSS, 2025 (acceptance rate: 15%).

7. Minghong Fang, Zhuqing Liu, Xuecen Zhao and Jia Liu, "Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing," in Proc. ACM TheWebConf (WWW), Sydney, Australia, Apr. 2025.(acceptance rate: 19.8%)

8. Wenbin Wang, Qiwen Ma, Zifan Zhang, Yuchen Liu, Zhuqing Liu, and Minghong Fang, "Poisoning Attacks and Defenses to Federated Unlearning," in Proc. ACM TheWebConf (WWW), Sydney, Australia, Apr. 2025.(acceptance rate: 19.8%)

9. Baolei Zhang*, Haoran Xin*, Minghong Fang, Zhuqing Liu, Biao Yi, Tong Li, and Zheli Liu. Traceback of Poisoning Attacks to Retrieval-Augmented Generation.  In Proc. The Web Conference (WWW), 2025 (*co-primary authors, acceptance rate: 19.8%).

10. Zhihao Dou, Xin Hu, Haibo Yang, Zhuqing Liu, and Minghong Fang. "Adversarial Attacks to Multi-Modal Models," In Proc. ACM LAMPS, 2024.

11. Zhuqing Liu, Xin Zhang, Jia Liu, Zhengyuan Zhu, and Songtao Lu, "PILOT: An O(1/T)-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation," in Proc. ICLR, Vienna, Austria, May. 2024, Spotlight (acceptance rate: 5%).

12. Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, and Jia Liu, "Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning," in Proc. ICML, Honolulu, HI, Jul. 2023 (acceptance rate: 27.9%).

13. Zhuqing Liu, Xin Zhang, Songtao Lu, and Jia Liu, "PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities," in Proc. ACM MobiHoc, Washington, DC, Oct. 2023 (acceptance rate: 21.9%).

14. Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, and Jia Liu,  "INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks," in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).

15. Zhuqing Liu, Xin Zhang, and Jia Liu, "SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters," in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).

16. Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, and Zhengyuan Zhu, "NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data," in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).

17. Haibo Yang, Zhuqing Liu, Xin Zhang, and Jia Liu, "SAGDA: Achieving O(ε-2) Communication Complexity in Federated Min-Max Learning," in Proc. NeurIPS, New Orleans, LA, Dec. 2022 (acceptance rate: 25.6%).

18. Zhuqing Liu*, Xin Zhang*, Jia Liu, Zhengyuan Zhu, and Songtao Lu, "Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning," in Proc. NeurIPS, Virtual Event, Dec. 2021 (acceptance rate: 26%).

19. Peiwen Qiu, Yining Li, Zhuqing Liu, Prashant Khanduri, Jia Liu, Ness B. Shroff, Elizabeth S. Bentley, and Kurt Turck, "DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization," in Proc. IEEE INFOCOM, New York City, NY, May 2023 (acceptance rate: 19.2%).

20. Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, and Michinari Momma "Federated Multi-Objective Learning," in Proc. NeurIPS, New Orleans, LA, Dec. 2023 (acceptance rate: 26.1%).

21. Luning Bi, Fei Tao, Ying Zuo, Zhuqing Liu. “Energy-aware material selection for product with multi-component under cloud environment”, ASME Journal of Computing and Information Science in Engineering, 2017.

22. Zhuqing Liu, Haibin Duan, Yijun Yang, Xiaoguang Hu. "Pendulum-like Oscillation Controller for UAV Based on Levy-flight Pigeon-inspired Optimization and LQR", IEEE Symposium Series on Computational Intelligence, 2016. (acceptance rate: 33%). 

*The authors are joint first authors with equal contributions.