ICML 2018oral788 citations
The Hidden Vulnerability of Distributed Learning in Byzantium
El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault
Abstract
While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent approaches have been proposed to ensure the robustness of distributed SGD against adversarial (Byzantine) workers sending
BibTeX
@InProceedings{pmlr-v80-mhamdi18a,
title = {The Hidden Vulnerability of Distributed Learning in {B}yzantium},
author = {El Mhamdi, El Mahdi and Guerraoui, Rachid and Rouault, S{\'e}bastien},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {3521--3530},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/mhamdi18a/mhamdi18a.pdf},
url = {https://proceedings.mlr.press/v80/mhamdi18a.html},
abstract = {While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent approaches have been proposed to ensure the robustness of distributed SGD against adversarial (Byzantine) workers sending