Adaptive Worker Grouping for Communication-Efficient and Straggler-Tolerant Distributed SGD. - Feng Zhu 025; Jingjing Zhang 002; Osvaldo Simeone; Xin Wang 003 (ISIT, 2022)

Communication-Efficient Local SGD with Age-Based Worker Selection. - Feng Zhu 025; Jingjing Zhang 002; Xin Wang 003 (CoRR, 2022)

DRAG: Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data. - Feng Zhu 025; Jingjing Zhang 002; Shengyun Liu; Xin Wang 003 (CoRR, 2023)

Communication-efficient local SGD with age-based worker selection. - Feng Zhu 025; Jingjing Zhang; Xin Wang (J. Supercomput., 2023)

Towards Fast Rates for Federated and Multi-Task Reinforcement Learning. - Feng Zhu 025; Robert W. Heath Jr.; Aritra Mitra (CDC, 2024)

STSyn: Speeding Up Local SGD With Straggler-Tolerant Synchronization. - Feng Zhu 025; Jingjing Zhang 002; Xin Wang 003 (IEEE Trans. Signal Process., 2024)

Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling. - Feng Zhu 025; Aritra Mitra; Robert W. Heath Jr. (CoRR, 2025)