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The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication

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arxiv 2102.01583 v2 pith:SSOWXDDM submitted 2021-02-02 cs.LG math.OC

Blake Woodworth , Brian Bullins , Ohad Shamir , Nathan Srebro This is my paper · ORCID
classification cs.LGmath.OC
keywords communicationstochasticboundcomplexityconvexdistributedintermittentmin-max
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abstract

We resolve the min-max complexity of distributed stochastic convex optimization (up to a log factor) in the intermittent communication setting, where $M$ machines work in parallel over the course of $R$ rounds of communication to optimize the objective, and during each round of communication, each machine may sequentially compute $K$ stochastic gradient estimates. We present a novel lower bound with a matching upper bound that establishes an optimal algorithm.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness

    cs.LG 2025-05 conditional novelty 7.0 of 10

    AdaGrad-type algorithms provably need a complexity quadratic in the initial gap and smoothness constants under relaxed smoothness, so they cannot match the optimal rate of clipped SGD.

  2. Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization

    cs.LG 2026-08 conditional novelty 4.0 of 10

    A thesis proving communication-acceleration guarantees for local-step, compressed, Byzantine-robust, and low-rank federated optimization methods, assembled from the author's own published papers.

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