An introduction to decentralized stochastic optimization with gradient tracking

Understand

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 reduction, 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.