Stochastic Gradient Coding for Straggler Mitigation in Distributed Learning

TL;DR

This paper introduces Stochastic Gradient Coding (SGC), a new method for distributed gradient descent that effectively mitigates the impact of random stragglers by using redundancy and ignoring slow workers, maintaining convergence rates.

Contribution

SGC is a novel approximate gradient coding scheme that handles random stragglers efficiently, matching SGD convergence rates and outperforming existing codes with high straggler counts.

Findings

SGC achieves convergence rates similar to standard SGD.

A small amount of redundancy suffices to handle many stragglers.

Empirically outperforms existing codes with large numbers of stragglers.

Abstract

We consider distributed gradient descent in the presence of stragglers. Recent work on \em gradient coding \em and \em approximate gradient coding \em have shown how to add redundancy in distributed gradient descent to guarantee convergence even if some workers are \em stragglers\em---that is, slow or non-responsive. In this work we propose an approximate gradient coding scheme called \em Stochastic Gradient Coding \em (SGC), which works when the stragglers are random. SGC distributes data points redundantly to workers according to a pair-wise balanced design, and then simply ignores the stragglers. We prove that the convergence rate of SGC mirrors that of batched Stochastic Gradient Descent (SGD) for the $\ell_2$ loss function, and show how the convergence rate can improve with the redundancy. We also provide bounds for more general convex loss functions. We show empirically that SGC requires a small amount of redundancy to handle a large number of stragglers and that it can outperform existing approximate gradient codes when the number of stragglers is large.