Stochastic Gradient Tracking over Time-Varying Networks: One-Step Lyapunov Analysis

Sulaiman A. Alghunaim

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arXiv · 2608.16271

Stochastic Gradient Tracking over Time-Varying Networks: One-Step Lyapunov Analysis

Sulaiman A. Alghunaim

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Abstract

We study decentralized stochastic gradient tracking over a time-varying network of $N$ agents under a uniform window-mixing condition. Products of $\tau$ consecutive doubly stochastic mixing matrices contract disagreement by a factor $\lambda<1$, although individual matrices need not contract disagreement strictly and individual communication graphs may be disconnected. We construct a time-varying quadratic norm that turns this window contraction into an exact one-step Lyapunov identity. This leads to coupled one-step recursions for the centroid and disagreement errors, without unrolling the dynamics over communication windows. For smooth strongly convex objectives, the leading stochastic term is $\widetilde{\mathcal O}(1/(NK))$; for smooth convex objectives, it is $\mathcal O(1/\sqrt{NK})$. Both match their centralized mini-batch counterparts and yield linear speedup after a network-dependent transient.

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BibTeX RIS

Sulaiman A. Alghunaim. 2026-08-17. Stochastic Gradient Tracking over Time-Varying Networks: One-Step Lyapunov Analysis. https://arxiv.org/abs/2608.16271

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