# A continuous-time analysis of distributed stochastic gradient

## TL;DR

This paper studies how synchronization in distributed stochastic gradient algorithms reduces noise and improves convergence, using a biological analogy and providing theoretical and empirical evidence on non-convex objectives and neural networks.

## Contribution

It introduces a quorum sensing-inspired analysis of synchronization effects, derives convergence bounds, and explores new algorithms with regularizing properties for distributed and non-distributed optimization.

## Key findings

- Synchronization reduces noise in distributed SGD.
- Coupling stabilizes higher noise levels and enhances convergence.
- EASGD exhibits a surprising regularizing effect even in non-distributed settings.

## Abstract

We analyze the effect of synchronization on distributed stochastic gradient algorithms. By exploiting an analogy with dynamical models of biological quorum sensing - where synchronization between agents is induced through communication with a common signal - we quantify how synchronization can significantly reduce the magnitude of the noise felt by the individual distributed agents and by their spatial mean. This noise reduction is in turn associated with a reduction in the smoothing of the loss function imposed by the stochastic gradient approximation. Through simulations on model non-convex objectives, we demonstrate that coupling can stabilize higher noise levels and improve convergence. We provide a convergence analysis for strongly convex functions by deriving a bound on the expected deviation of the spatial mean of the agents from the global minimizer for an algorithm based on quorum sensing, the same algorithm with momentum, and the Elastic Averaging SGD (EASGD) algorithm. We discuss extensions to new algorithms that allow each agent to broadcast its current measure of success and shape the collective computation accordingly. We supplement our theoretical analysis with numerical experiments on convolutional neural networks trained on the CIFAR-10 dataset, where we note a surprising regularizing property of EASGD even when applied to the non-distributed case. This observation suggests alternative second-order in-time algorithms for non-distributed optimization that are competitive with momentum methods.