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

SignSGD: Fault-Tolerance to Blind and Byzantine Adversaries