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Distributed optimization methods are actively researched by optimizationcommunity. Due to applications in distributed machine learning, modern researchdirections include stochastic objectives, reducing communication frequency, andtime-varying communication network topology. Recently, an analysis unifyingseveral centralized and decentralized approaches to stochastic distributedoptimization was developed in Koloskova et al. (2020). In this work, we employa Catalyst framework and accelerate the rates of Koloskova et al. (2020) in thecase of low stochastic noise.

An acceleration of decentralized SGD under general assumptions with low stochastic noise