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  4. Byzantine tolerant gradient descent for distributed machine learning with adversaries
 
patent

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Blanchard, Peva  
•
El Mhamdi, El Mahdi  
•
Guerraoui, Rachid  
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2019

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).

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Type
patent
EPO Family ID

60484385

Author(s)
Blanchard, Peva  
El Mhamdi, El Mahdi  
Guerraoui, Rachid  
Stainer, Julien  
Note

Alternative title(s) : (fr) Descente de gradient tolérant les byzantines pour apprentissage machine distribué avec des adversaires

TTO classification

TTO:6.1813

EPFL units
AVP-R-TTO  
DCL  
IdentifierCountry codeKind codeDate issued

US2020380340

US

A1

2020-12-03

WO2019105543

WO

A1

2019-06-06

Available on Infoscience
December 5, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/163760
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