[2603.02639] Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need

Mathematics > Optimization and Control arXiv:2603.02639 (math) [Submitted on 3 Mar 2026] Title:Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need Authors:Xinran Zheng, Tara Javidi, Behrouz Touri View a PDF of the paper titled Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need, by Xinran Zheng and 2 other authors View PDF Abstract:We propose a general framework for distributed stochastic optimization under delayed gradient models. In this setting, $n$ local agents leverage their own data and computation to assist a central server in minimizing a global objective composed of agents' local cost functions. Each agent is allowed to transmit stochastic-potentially biased and delayed-estimates of its local gradient. While a prior work has advocated delay-adaptive step sizes for stochastic gradient descent (SGD) in the presence of delays, we demonstrate that a pre-chosen diminishing step size is sufficient and matches the performance of the adaptive scheme. Moreover, our analysis establishes that diminishing step sizes recover the optimal SGD rates for nonconvex and strongly convex objectives. Subjects: Optimization and Control (math.OC); Machine Learning (cs.LG) Cite as: arXiv:2603.02639 [math.OC]   (or arXiv:2603.02639v1 [math.OC] for this version)   https://doi.org/10.48550/arXiv.2603.02639 Focus to learn more arXiv-issued DOI via DataCite (pending registrat...