Publication

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Rachid Guerraoui, El Mahdi El Mhamdi, Julien Michel Stainer, Peva François Blanchard
2019
Patents

Abstract

The present application concerns a computer-implemented method for training a machine learning model in a distributed fashion, using Stochastic Gradient Descent, SGD, wherein the method is performed by a first computer in a distributed computing environment and comprises performing a learning round, comprising broadcasting a parameter vector to a plurality of worker computers in the distributed computing environment, receiving an estimate update vector (gradient) from all or a subset of the worker computers, wherein each received estimate vector is either an estimate of a gradient of a cost function, or an erroneous vector, and determining an updated parameter vector for use in a next learning round based only on a subset of the received estimate vectors. The method aggregates the gradients while guaranteeing resilience to up to half workers being compromised (malfunctioning, erroneous or modified by attackers).

Official source

https://infoscience.epfl.ch/entities/publication/114ac2a3-7867-4d29-bd49-5e14c2585f59

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Rachid Guerraoui, El Mahdi El Mhamdi, Julien Michel Stainer, Peva François Blanchard
2019
Patents

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/114ac2a3-7867-4d29-bd49-5e14c2585f59

About this result

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In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.

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