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

Blake E Woodworth , Brian Bullins , Ohad Shamir , Nathan Srebro

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Session: Paper Awards

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Poster: Poster Session 4

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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