Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate

Understand

Driven by the need to solve increasingly complex optimization problems in signal processing and machine learning, there has been increasing interest in understanding the behavior of gradient-descent algorithms in non-convex environments.

• Most available works on distributed non-convex optimization problems focus on the deterministic setting where exact gradients are available at each agent.
• In this work and its Part II, we consider stochastic cost functions, where exact gradients are replaced by stochastic approximations and the resulting gradient noise persistently seeps into the dynamics of the algorithm.
• We establish that the diffusion learning strategy continues to yield meaningful estimates non-convex scenarios in the sense that the iterates by the individual agents will cluster in a small region around the network centroid.