22 citations · 56 across the 13 of their papers we have counts for
5 papers · 1 filter
Distributed Online Randomized Gradient-Free Optimization with Compressed Communication
Longkang Zhu, Xinli Shi, Xiangping Xu +2
This paper addresses two fundamental challenges in distributed online convex optimization: communication efficiency and optimization under limited feedback. We propose a unified fr…
Distributed Online Randomized Gradient-Free optimization with Compressed Communication
Longkang Zhu, Xinli Shi, Xiangping Xu +1
This paper addresses two fundamental challenges in distributed online convex optimization: communication efficiency and optimization under limited feedback. We propose Online Compr…
Perturbed Proximal Gradient ADMM for Nonconvex Composite Optimization
Yuan Zhou, Xinli Shi, Luyao Guo +2
This paper proposes a Perturbed Proximal Gradient ADMM (PPG-ADMM) framework for solving general nonconvex composite optimization problems, where the objective function consists of…
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…
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…