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

Federated Model Decomposition with Private Vocabulary for Text Classification. - Zhuo Zhang 007; Xiangjing Hu; Lizhen Qu; Qifan Wang 001; Zenglin Xu (EMNLP, 2022)

FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP. - Zhuo Zhang 007; Xiangjing Hu; Jingyuan Zhang; Yating Zhang; Hui Wang 013; Lizhen Qu; Zenglin Xu (ACL (1), 2023)

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

Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily. - Langzhang Liang; Xiangjing Hu; Zenglin Xu; Zixing Song; Irwin King (NeurIPS, 2023)

Stochastic Clustered Federated Learning. - Dun Zeng; Xiangjing Hu; Shiyu Liu; Yue Yu 001; Qifan Wang 001; 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)

Revisiting Data Reconstruction Attacks on Real-world Dataset for Federated Natural Language Understanding. - Zhuo Zhang 007; Jintao Huang; Xiangjing Hu; Jingyuan Zhang; Yating Zhang; Hui Wang 013; Yue Yu 001; Qifan Wang 001; Lizhen Qu; Zenglin Xu (LREC/COLING, 2024)

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)