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.
@article{arxiv.2011.07585,
title = {An acceleration of decentralized SGD under general assumptions with low stochastic noise},
author = {Trimbach Ekaterina and Rogozin Alexander},
journal= {arXiv preprint arXiv:2011.07585},
year = {2021}
}