8 papers
Local adapt-then-combine algorithms for distributed nonsmooth optimization: Achieving provable communication acceleration
Luyao Guo, Xinli Shi, Wenying Xu +1
This paper is concerned with the distributed composite optimization problem over networks, where agents aim to minimize a sum of local smooth components and a common nonsmooth term…
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…
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…
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…
A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization
Luyao Guo, Luqing Wang, Xinli Shi +1
Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform sever…
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…