Byzantine machine learning

The present invention concerns computer-implemented methods for training a machine learning model using Stochastic Gradient Descent, SGD. In one embodiment, 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, and upon receipt of one or more respective estimate vectors from a subset of the worker computers, determining an updated parameter vector for use in a next learning round based on the one or more received estimate vectors, wherein the determining comprises ignoring an estimate vector received from a given worker computer when a sending frequency of the given worker computer is above a threshold value. The method aggregates the gradients in an asynchronous communication model with unbounded communication delays.

Alternative title(s) : (fr) Apprentissage automatique byzantin
Other identifiers:
EPO Family ID: 62981189
Patent number(s):

 Record created 2020-02-03, last modified 2020-10-29

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