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

## TL;DR

This paper analyzes the convergence behavior of distributed stochastic gradient algorithms in non-convex environments, showing that agents' estimates cluster around a network centroid at a linear rate.

## Contribution

It extends existing non-convex distributed optimization analysis to stochastic settings, demonstrating clustering and convergence properties under gradient noise.

## Key findings

- Agents' estimates cluster around the network centroid.
- The diffusion strategy converges at a linear rate.
- The approach handles persistent gradient noise in non-convex optimization.

## Abstract

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. We use this insight to motivate a short-term model for network evolution over a finite-horizon. In Part II [2] of this work, we leverage this model to establish descent of the diffusion strategy through saddle points in O(1/$\mu$) steps and the return of approximately second-order stationary points in a polynomial number of iterations.