FedLab: A Flexible Federated Learning Framework. - Dun Zeng; Siqi Liang 001; Xiangjing Hu; Zenglin Xu (CoRR, 2021)

Aggregating Gradients in Encoded Domain for Federated Learning. - Dun Zeng; Shiyu Liu; Zenglin Xu (CoRR, 2022)

Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization. - Maolin Wang 001; Dun Zeng; Zenglin Xu; Ruocheng Guo; Xiangyu Zhao 001 (ICDM, 2023)

Flexible Contribution Estimation Methods for Horizontal Federated Learning. - Xiangjing Hu; Congjian Luo; Dun Zeng; Zenglin Xu; Ping Guo 002; Irwin King (IJCNN, 2023)

A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy. - Yifei Zhang; Dun Zeng; Jinglong Luo; Zenglin Xu; Irwin King (WWW (Companion Volume), 2023)

Stochastic Clustered Federated Learning. - Dun Zeng; Xiangjing Hu; Shiyu Liu; Yue Yu 001; Qifan Wang 001; Zenglin Xu (CoRR, 2023)

FedNoisy: Federated Noisy Label Learning Benchmark. - Siqi Liang 001; Jintao Huang; Dun Zeng; Junyuan Hong; Jiayu Zhou; Zenglin Xu (CoRR, 2023)

Personalized Federated Learning via Amortized Bayesian Meta-Learning. - Shiyu Liu; Shaogao Lv; Dun Zeng; Zenglin Xu; Hui Wang 013; Yue Yu 001 (CoRR, 2023)

Exploring Federated Optimization by Reducing Variance of Adaptive Unbiased Client Sampling. - Dun Zeng; Zenglin Xu; Yu Pan 005; Qifan Wang 001; Xiaoying Tang 002 (CoRR, 2023)

Tackling Hybrid Heterogeneity on Federated Optimization via Gradient Diversity Maximization. - Dun Zeng; Zenglin Xu; Yu Pan 005; Qifan Wang 001; Xiaoying Tang 002 (CoRR, 2023)

Federated Generalization via Information-Theoretic Distribution Diversification. - Zheshun Wu; Zenglin Xu; Dun Zeng; Qifan Wang 001 (CoRR, 2023)

On Diversified Preferences of Large Language Model Alignment. - Dun Zeng; Yong Dai 001; Pengyu Cheng; Tianhao Hu; Wanshun Chen; Nan Du; Zenglin Xu (CoRR, 2023)

FedLab: A Flexible Federated Learning Framework. - Dun Zeng; Siqi Liang 001; Xiangjing Hu; Hui Wang 013; Zenglin Xu (J. Mach. Learn. Res., 2023)

On Diversified Preferences of Large Language Model Alignment. - Dun Zeng; Yong Dai 001; Pengyu Cheng; Longyue Wang; Tianhao Hu; Wanshun Chen; Nan Du; Zenglin Xu (EMNLP (Findings), 2024)

FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data. - Yukun Zhang; Guanzhong Chen; Zenglin Xu; Jianyong Wang 001; Dun Zeng; Junfan Li; Jinghua Wang; Yuan Qi 001; Irwin King (CoRR, 2024)

Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization. - Dun Zeng; Zheshun Wu; Shiyu Liu; Yu Pan 005; Xiaoying Tang 002; Zenglin Xu (CoRR, 2024)

Topology Learning for Heterogeneous Decentralized Federated Learning Over Unreliable D2D Networks. - Zheshun Wu; Zenglin Xu; Dun Zeng; Junfan Li; Jie Liu 001 (IEEE Trans. Veh. Technol., 2024)

A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges. - Yifei Zhang; Dun Zeng; Jinglong Luo; Xinyu Fu 004; Guanzhong Chen; Zenglin Xu; Irwin King (ACM Trans. Intell. Syst. Technol., 2024)

On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond. - Dun Zeng; Zenglin Xu; Shiyu Liu; Yu Pan 005; Qifan Wang 001; Xiaoying Tang 002 (AISTATS, 2025)

StoCFL: A stochastically clustered federated learning framework for Non-IID data with dynamic client participation. - Dun Zeng; Xiangjing Hu; Shiyu Liu; Yue Yu 001; Qifan Wang 001; Zenglin Xu (Neural Networks, 2025)

Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance. - Dun Zeng; Zenglin Xu; Yu Pan 005; Xu Luo 003; Qifan Wang 001; Xiaoying Tang 002 (Trans. Mach. Learn. Res., 2025)

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward. - Han Weng; Boyi Liu; Yuanfeng Song; Dun Zeng; Yingxiang Yang; Yi Zhan; Longjie Cui; Xiaoming Yin; Yang Sun (CoRR, 2025)