  Distributed optimization methods are actively researched by optimization
community. Due to applications in distributed machine learning, modern research
directions include stochastic objectives, reducing communication frequency, and
time-varying communication network topology. Recently, an analysis unifying
several centralized and decentralized approaches to stochastic distributed
optimization was developed in Koloskova et al. (2020). In this work, we employ
a Catalyst framework and accelerate the rates of Koloskova et al. (2020) in the
case of low stochastic noise.
