# Gradient Coding

arXiv: https://arxiv.org/abs/1612.03301 · HTML: https://mlsanity.com/paper/1612.03301/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2016.

## Built on by (most cited first, 1 of 1)

- [Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent](https://mlsanity.com/paper/2201.12990/) (2022)

## Abstract (arXiv)

We propose a novel coding theoretic framework for mitigating stragglers in distributed learning. We show how carefully replicating data blocks and coding across gradients can provide tolerance to failures and stragglers for Synchronous Gradient Descent. We implement our schemes in python (using MPI) to run on Amazon EC2, and show how we compare against baseline approaches in running time and generalization error.