On the convergence rate of distributed gradient methods for finite-sum optimization under communication delays

Understand

Motivated by applications in machine learning and statistics, we study distributed optimization problems over a network of processors, where the goal is to optimize a global objective composed of a sum of local functions.

• In these problems, due to the large scale of the data sets, the data and computation must be distributed over processors resulting in the need for distributed algorithms.
• In this paper, we consider a popular distributed gradient-based consensus algorithm, which only requires local computation and communication.
• An important problem in this area is to analyze the convergence rate of such algorithms in the presence of communication delays that are inevitable in distributed systems.