# Numerically Stable Binary Gradient Coding

arXiv: https://arxiv.org/abs/2001.11449 · HTML: https://mlsanity.com/paper/2001.11449/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier](https://mlsanity.com/paper/2101.11653/) (2021)
- [Gradient Coding with Iterative Block Leverage Score Sampling](https://mlsanity.com/paper/2308.03096/) (2023)
- [Iterative Sketching for Secure Coded Regression](https://mlsanity.com/paper/2308.04185/) (2023)

## Abstract (arXiv)

A major hurdle in machine learning is scalability to massive datasets. One approach to overcoming this is to distribute the computational tasks among several workers. textitGradient coding has been recently proposed in distributed optimization to compute the gradient of an objective function using multiple, possibly unreliable, worker nodes. By designing distributed coded schemes, gradient coded computations can be made resilient to textitstragglers, nodes with longer response time comparing to other nodes in a distributed network. Most such schemes rely on operations over the real or complex numbers and are inherently numerically unstable. We present a binary scheme which avoids such operations, thereby enabling numerically stable distributed computation of the gradient. Also, some restricting assumptions in prior work are dropped, and a more efficient decoding is given.