3 papers
cs.LG2025
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
Yuan Zhou, Jiachen Zhong, Xinli Shi +2
Composite federated learning offers a general framework for solving machine learning problems with additional regularization terms. However, existing methods often face significant…
cs.LG2025
A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
Xinli Shi, Xingxing Yuan, Longkang Zhu +1
With the increasing scale and dynamics of data, distributed online optimization has become essential for real-time decision-making in various applications. However, existing algori…
cs.LG2025
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
Yuan Zhou, Xinli Shi, Xuelong Li +3
Decentralized Federated Learning (DFL) enables collaborative model training without relying on a central server. When local objectives are nonconvex and coupled with nonsmooth weak…