Publications

Also available on Google Scholar. Names are listed in the order they appear in each paper; an asterisk (*) marks joint first authorship.

2026

  1. "Patcher: Post-Hoc Patching of Backdoored Large Language Models," Anjun Gao, Yueyang Quan, Yufei Xia, Zhuqing Liu, and Minghong Fang, in Proc. USENIX Security Symposium, 2026.
  2. "Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation," Baolei Zhang, Haoran Xin, Yuxi Chen, Zhuqing Liu, Biao Yi, Tong Li, Lihai Nie, Zheli Liu, and Minghong Fang, in Proc. IEEE Symposium on Security and Privacy, 2026 (acceptance rate: 13%).
  3. "Beware What You Autocomplete: Forensic Attribution of Backdoored Code Completions," Anjun Gao, Yueyang Quan, Zhuqing Liu, and Minghong Fang, in Proc. COLM, 2026 (acceptance rate: 29%).
  4. "Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems," Yufei Xia, Anjun Gao, Yueyang Quan, Zhuqing Liu, and Minghong Fang, in Proc. COLM, 2026 (acceptance rate: 29%).
  5. "SecureSplit: Mitigating Backdoor Attacks in Split Learning," Zhihao Dou, Dongfei Cui, Weida Wang, Anjun Gao, Yueyang Quan, Mengyao Ma, Viet Vo, Guangdong Bai, Zhuqing Liu, and Minghong Fang, in Proc. The Web Conference (WWW), 2026 (acceptance rate: 20.1%).
  6. "Multi-Objective Bilevel Learning," Zhiyao Zhang, Zhuqing Liu, Xin Zhang, Wen-Yen Chen, Jiyan Yang, and Jia Liu, in Proc. AAAI, Singapore, Jan. 2026 (acceptance rate: 17.6%).
  7. "SecureAFL: Secure Asynchronous Federated Learning," Anjun Gao*, Feng Wang*, Zhenglin Wan, Yueyang Quan, Zhuqing Liu, and Minghong Fang, in Proc. ACM AsiaCCS, 2026.
  8. "When the Server Steps In: Calibrated Updates for Fair Federated Learning," Tianrun Yu*, Kaixiang Zhao*, Cheng Zhang, Anjun Gao, Yueyang Quan, Zhuqing Liu, and Minghong Fang, in Proc. WiOpt, 2026.
  9. "Practical Framework for Privacy-Preserving and Byzantine-Robust Federated Learning," Baolei Zhang, Minghong Fang, Zhuqing Liu, Biao Yi, Peizhao Zhou, Yuan Wang, Tong Li, and Zheli Liu, in IEEE Transactions on Information Forensics and Security, 2026.

2025

  1. "DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity," Zhen Qin, Zhuqing Liu, Songtao Lu, Yingbin Liang, and Jia Liu, in Proc. ICLR, Singapore, Apr. 2025 (acceptance rate: 32%).
  2. "Do We Really Need to Design New Byzantine-Robust Aggregation Rules?," Minghong Fang, Seyedsina Nabavirazavi, Wei Sun, Zhuqing Liu, Sundararaja Iyengar, and Haibo Yang, in Proc. NDSS, 2025 (acceptance rate: 15%).
  3. "STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning," Zhuqing Liu, Chaosheng Dong, Michinari Momma, Simone Shao, Shaoyuan Xu, Haibo Yang, and Jia Liu, in Proc. UAI, Rio de Janeiro, Brazil, 2025 (acceptance rate: 31%).
  4. "Poisoning Attacks and Defenses to Federated Unlearning," Wenbin Wang, Qiwen Ma, Zifan Zhang, Yuchen Liu, Zhuqing Liu, and Minghong Fang, in Proc. ACM TheWebConf (WWW), Sydney, Australia, Apr. 2025 (acceptance rate: 19.8%).
  5. "Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing," Minghong Fang, Zhuqing Liu, Xuecen Zhao, and Jia Liu, in Proc. ACM TheWebConf (WWW), Sydney, Australia, Apr. 2025 (acceptance rate: 19.8%).
  6. "Traceback of Poisoning Attacks to Retrieval-Augmented Generation," Baolei Zhang, Haoran Xin, Minghong Fang, Zhuqing Liu, Biao Yi, Tong Li, and Zheli Liu, in Proc. ACM TheWebConf (WWW), Sydney, Australia, Apr. 2025 (acceptance rate: 19.8%).
  7. "Toward Malicious Clients Detection in Federated Learning," Zhihao Dou, Jiaqi Wang, Wei Sun, Zhuqing Liu, and Minghong Fang, in Proc. ACM AsiaCCS, 2025 (acceptance rate: 20.4%).
  8. "Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach," Yueyang Quan, Chang Wang, Shengjie Zhai, Minghong Fang, and Zhuqing Liu, in Proc. ACM MobiHoc, Oct. 2025 (acceptance rate: 23%).
  9. "Secure Retrieval-Augmented Generation against Poisoning Attacks," Zirui Cheng, Jikai Sun, Anjun Gao, Yueyang Quan, Zhuqing Liu, Xiaohua Hu, and Minghong Fang, in Proc. IEEE BigData, 2025.
  10. "Fairness-Constrained Optimization Attack in Federated Learning," Harsh Kasyap, Minghong Fang, Zhuqing Liu, Carsten Maple, and Somanath Tripathy, in Proc. IEEE TrustCom, 2025.
  11. "Benchmarking Poisoning Attacks against Retrieval-Augmented Generation," Baolei Zhang, Haoran Xin, Jiatong Li, Dongzhe Zhang, Minghong Fang, Zhuqing Liu, Lihai Nie, and Zheli Liu, in arXiv preprint arXiv:2505.18543, 2025.
  12. "Practical Poisoning Attacks against Retrieval-Augmented Generation," Baolei Zhang, Yuxi Chen, Minghong Fang, Zhuqing Liu, Lihai Nie, Tong Li, and Zheli Liu, in arXiv preprint arXiv:2504.03957, 2025.

2024

  1. "PILOT: An O(1/T)-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation," Zhuqing Liu, Xin Zhang, Jia Liu, Zhengyuan Zhu, and Songtao Lu, Spotlight, in Proc. ICLR, Vienna, Austria, May 2024 (spotlight rate: 5%).
  2. "Adversarial Attacks to Multi-Modal Models," Zhihao Dou, Xin Hu, Haibo Yang, Zhuqing Liu, and Minghong Fang, in Proc. ACM LAMPS, Salt Lake City, UT, Oct. 2024 (acceptance rate: 19%).

2023

  1. "Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning," Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, and Jia Liu, in Proc. ICML, Honolulu, HI, Jul. 2023 (acceptance rate: 27.9%).
  2. "Federated Multi-Objective Learning," Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, and Michinari Momma, in Proc. NeurIPS, New Orleans, LA, Dec. 2023 (acceptance rate: 26.1%).
  3. "PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities," Zhuqing Liu, Xin Zhang, Jia Liu, and Songtao Lu, in Proc. ACM MobiHoc, Washington, DC, Oct. 2023 (acceptance rate: 21.9%).
  4. "DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization," Peiwen Qiu, Yining Li, Zhuqing Liu, Prashant Khanduri, Jia Liu, Ness B. Shroff, Elizabeth S. Bentley, and Kurt Turck, in Proc. IEEE INFOCOM, New York City, NY, May 2023 (acceptance rate: 19.2%).

2022

  1. "SAGDA: Achieving O(ε−2) Communication Complexity in Federated Min-Max Learning," Haibo Yang, Zhuqing Liu, Xin Zhang, and Jia Liu, in Proc. NeurIPS, New Orleans, LA, Dec. 2022 (acceptance rate: 25.6%).
  2. "INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks," Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, and Jia Liu, in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).
  3. "SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters," Zhuqing Liu, Xin Zhang, and Jia Liu, in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).
  4. "NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data," Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, and Zhengyuan Zhu, in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).

2021 and Earlier

  1. "Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning," Zhuqing Liu*, Xin Zhang*, Jia Liu, Zhengyuan Zhu, and Songtao Lu, in Proc. NeurIPS, Virtual Event, Dec. 2021 (acceptance rate: 26%).
  2. "Energy-Aware Material Selection for Product with Multi-Component under Cloud Environment," Luning Bi, Fei Tao, Ying Zuo, and Zhuqing Liu, in ASME Journal of Computing and Information Science in Engineering, 2017.
  3. "Pendulum-Like Oscillation Controller for UAV Based on Levy-Flight Pigeon-Inspired Optimization and LQR," Zhuqing Liu, Haibin Duan, Yijun Yang, and Xiaoguang Hu, in IEEE Symposium Series on Computational Intelligence, 2016 (acceptance rate: 33%).

* These authors contributed equally to this work.

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