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

• URL: http://arxiv.org/abs/2102.01583v1
• Date: Tue, 2 Feb 2021 16:18:29 GMT
• Title: The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication
• Authors: Blake Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
• Abstract summary: We resolve the min-max complexity of distributed convex optimization (up to a log factor) in the intermittent communication setting. We present a novel lower bound with a matching upper bound that establishes an optimal algorithm.
• Score: 61.60069865891783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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