ICML 2022spotlight76 citations
Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
Abstract
Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence of some misbehaving (a.k.a.,
BibTeX
@InProceedings{pmlr-v162-farhadkhani22a,
title = {{B}yzantine Machine Learning Made Easy By Resilient Averaging of Momentums},
author = {Farhadkhani, Sadegh and Guerraoui, Rachid and Gupta, Nirupam and Pinot, Rafael and Stephan, John},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {6246--6283},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/farhadkhani22a/farhadkhani22a.pdf},
url = {https://proceedings.mlr.press/v162/farhadkhani22a.html},
abstract = {Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence of some misbehaving (a.k.a.,