# SignSGD: Fault-Tolerance to Blind and Byzantine Adversaries

arXiv: https://arxiv.org/abs/2202.02085 · HTML: https://mlsanity.com/paper/2202.02085/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks](https://mlsanity.com/paper/1910.06093/) (2019)

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

- [Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation](https://mlsanity.com/paper/2308.02881/) (2023)

## Abstract (arXiv)

Distributed learning has become a necessity for training ever-growing models by sharing calculation among several devices. However, some of the devices can be faulty, deliberately or not, preventing the proper convergence. As a matter of fact, the baseline distributed SGD algorithm does not converge in the presence of one Byzantine adversary. In this article we focus on the more robust SignSGD algorithm derived from SGD. We provide an upper bound for the convergence rate of SignSGD proving that this new version is robust to Byzantine adversaries. We implemented SignSGD along with Byzantine strategies attempting to crush the learning process. Therefore, we provide empirical observations from our experiments to support our theory. Our code is available on GitHub https://github.com/jasonakoun/signsgd-fault-tolerance and our experiments are reproducible by using the provided parameters.