Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology. - Yan Huang 036; Ying Sun 003; Zehan Zhu; Changzhi Yan; Jinming Xu 002 (ICML, 2022)

Asynchronous Byzantine-Robust Stochastic Aggregation with Variance Reduction for Distributed Learning. - Zehan Zhu; Yan Huang 036; Chengcheng Zhao; Jinming Xu 002 (CDC, 2023)

Robust Fully-Asynchronous Methods for Distributed Training over General Architecture. - Zehan Zhu; Ye Tian 021; Yan Huang 036; Jinming Xu 002; Shibo He (CoRR, 2023)

PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds. - Zehan Zhu; Yan Huang 036; Xin Wang; Jinming Xu 002 (IJCAI, 2024)

PerfTop: Towards performance prediction of distributed learning over general topology. - Changzhi Yan; Zehan Zhu; Youcheng Niu; Cong Wang 040; Cheng Zhuo; Jinming Xu 002 (J. Parallel Distributed Comput., 2024)

R-FAST: Robust Fully-Asynchronous Stochastic Gradient Tracking Over General Topology. - Zehan Zhu; Ye Tian 021; Yan Huang 036; Jinming Xu 002; Shibo He (IEEE Trans. Signal Inf. Process. over Networks, 2024)

Dyn-D2P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee. - Zehan Zhu; Yan Huang 036; Xin Wang; Shouling Ji; Jinming Xu 002 (CoRR, 2025)