An introduction to decentralized stochastic optimization with gradient tracking [article]

Ran Xin and Soummya Kar and Usman A. Khan
2019 arXiv   pre-print
Decentralized solutions to finite-sum minimization are of significant importance in many signal processing, control, and machine learning applications. In such settings, the data is distributed over a network of arbitrarily-connected nodes and raw data sharing is prohibitive often due to communication or privacy constraints. In this article, we review decentralized stochastic first-order optimization methods and illustrate some recent improvements based on gradient tracking and variance
more » ... n, focusing particularly on smooth and strongly-convex objective functions. We provide intuitive illustrations of the main technical ideas as well as applications of the algorithms in the context of decentralized training of machine learning models.
arXiv:1907.09648v2 fatcat:rizav6qrm5fh3ip63b6lg3tbse