most citedGradient and Variable Tracking with Multiple Local SGD for Decentralized Non-Convex Learning

2 citations · 3 across the 2 of their papers we have counts for

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2 papers

math.OC2023★ 2 cited

Gradient and Variable Tracking with Multiple Local SGD for Decentralized Non-Convex Learning

Songyang Ge, Tsung-Hui Chang

Stochastic distributed optimization methods that solve an optimization problem over a multi-agent network have played an important role in a variety of large-scale signal processin…

math.OC2021★ 1 cited

Decentralized Non-Convex Learning with Linearly Coupled Constraints

Jiawei Zhang, Songyang Ge, Tsung-Hui Chang +1

Motivated by the need for decentralized learning, this paper aims at designing a distributed algorithm for solving nonconvex problems with general linear constraints over a multi-a…