Methods for Decentralized Signal Processing With Big Data

Decentralized signal processing methods offer an attractive solution to perform big data analytics by harnessing distributed databases, servers, and network resources in a flexible and adaptive fashion. Often, machine learning algorithms on big data are instances of high-dimensional constrained optimization problems, where the projection step comes at a significant computational cost. In this context, the goal of this chapter is to review recent advances in the solution and analysis of decentralized projection-free algorithms, focusing on both convex and nonconvex problems and comparing them with decentralized projective gradient methods. The popular machine learning problem of robust low rank matrix completion is considered to validate our theoretical claims numerically.