Convex and Non-convex Federated Learning with Stale Stochastic Gradients: Diminishing Step Size is All You Need
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 re...
| Authors | Xinran Zheng, Tara Javidi, Behrouz Touri |
|---|---|
| Primary category | math.OC |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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