[1] Peng Tang, Xiaoyu Zhu, et al. “FLAD: Byzantine-Robust Federated Learning Based on Gradient Feature Anomaly Detection,” in IEEE Transactions on Dependable and Secure Computing, 2025.

[2] Ke Hu, Liyao Xiang, Peng Tang, and Weidong Qiu, “Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains,” IJCAI 2024.

[3] Guowei Lin, Fei Tang, Chaochao Cai, Jinyong Shan, Haiyang Xue, Wulu Li, Peng Tang, Xinyi Huang, and Weidong Qiu, “P²FRPSI: Privacy-Preserving Feature Retrieved Private Set Intersection,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 2201-2216

[4] Peng Tang, Weidong Qiu, Zheng Huang, Shuang Chen, Min Yan, Huijuan Lian, and Zhe Li. “Anomaly detection in electronic invoice systems based on machine learning,” Information Sciences, vol. 535, pp. 172–186, 2020.

[5] Peng Tang, Lifan Wang, Weidong Qiu, Zheng Huang, and Qiangmin Wang. “Fuzzy Clustered Federated Learning Under Mixed Data Distributions,” AAMAS 2024.

[1]上海市科技创新行动计划，场景驱动的联邦学习性能提升技术研究与验证，2024.10-2025.09，项目负责人

[2]重点研发计划，面向数据可信确权与交易的安全保障技术，2023.12-2026.11，子课题负责人

IEEE PCDS 2024、2025，TPC