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arXiv · 2609.39357

Robustifying Asynchronous SGD via Soft Throttling

Kaoru Otsuka·Maxime Meyer·Yuki Takezawa·Makoto Yamada·Anastasia Koloskova
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Abstract

Asynchronous SGD is a popular algorithm for distributed learning where each client's gradient update is applied on arrival. This leads to a speed-up, but also an increased vulnerability to attacks, as fast clients can dominate the total update. We introduce Throttle, a Byzantine-robust generalization of asynchronous SGD where the key idea is to exponentially down-weight updates from faster clients by a factor $q$. Both asynchronous SGD ($q=1$) and synchronous Byzantine-robust SGD ($q\to\infty$) correspond to specific settings of Throttle. We provide a theoretical analysis of the convergence rate and validate the robustness to attacks both theoretically and empirically. Remarkably, our experiments show that this down-weighting mechanism can also improve performance over standard asynchronous SGD even in the non-Byzantine setting.

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Kaoru Otsuka, Maxime Meyer, Yuki Takezawa, Makoto Yamada, Anastasia Koloskova. 2026-09-30. Robustifying Asynchronous SGD via Soft Throttling. https://arxiv.org/abs/2609.39357

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