Achieving Provable Byzantine Fault-tolerance in a Semi-honest Federated Learning Setting. - Xingxing Tang; Hanlin Gu; Lixin Fan; Qiang Yang 001 (PAKDD (2), 2023)

Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning. - Yan Kang 001; Hanlin Gu; Xingxing Tang; Yuanqin He; Yuzhu Zhang; Jinnan He; Yuxing Han 001; Lixin Fan; Kai Chen 005; Qiang Yang 001 (CoRR, 2023)

Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning. - Jinglin Liang; Jin Zhong; Hanlin Gu; Zhongqi Lu; Xingxing Tang; Gang Dai 002; Shuangping Huang; Lixin Fan; Qiang Yang 001 (ECCV (30), 2024)

FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders. - Hanlin Gu; Lixin Fan; Xingxing Tang; Qiang Yang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Optimizing Privacy, Utility, and Efficiency in a Constrained Multi-Objective Federated Learning Framework. - Yan Kang 001; Hanlin Gu; Xingxing Tang; Yuanqin He; Yuzhu Zhang; Jinnan He; Yuxing Han 001; Lixin Fan; Kai Chen 005; Qiang Yang 001 (ACM Trans. Intell. Syst. Technol., 2024)