3 papers
math.OC2026
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…
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
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…