# Improving Distributed Gradient Descent Using Reed-Solomon Codes

## TL;DR

This paper introduces a Reed-Solomon coding-based scheme to improve distributed gradient descent by efficiently mitigating stragglers, reducing delay, and optimizing performance in large-scale machine learning tasks.

## Contribution

It presents a deterministic coding scheme with an efficient decoding algorithm that minimizes the number of machines needed to recover the gradient, advancing distributed learning methods.

## Key findings

- The scheme recovers the gradient using the minimum number of machines theoretically possible.
- The delay model helps optimize parameters to reduce waiting time.
- Numerical results show the scheme's superiority over existing methods.

## Abstract

Today's massively-sized datasets have made it necessary to often perform computations on them in a distributed manner. In principle, a computational task is divided into subtasks which are distributed over a cluster operated by a taskmaster. One issue faced in practice is the delay incurred due to the presence of slow machines, known as \emph{stragglers}. Several schemes, including those based on replication, have been proposed in the literature to mitigate the effects of stragglers and more recently, those inspired by coding theory have begun to gain traction. In this work, we consider a distributed gradient descent setting suitable for a wide class of machine learning problems. We adapt the framework of Tandon et al. (arXiv:1612.03301) and present a deterministic scheme that, for a prescribed per-machine computational effort, recovers the gradient from the least number of machines $f$ theoretically permissible, via an $O(f^2)$ decoding algorithm. We also provide a theoretical delay model which can be used to minimize the expected waiting time per computation by optimally choosing the parameters of the scheme. Finally, we supplement our theoretical findings with numerical results that demonstrate the efficacy of the method and its advantages over competing schemes.