Publication

Brief Announcement: Byzantine-Tolerant Machine Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Peva François Blanchard, Julien Michel Stainer
2017
Conference Papers

Abstract

We report on \emph{Krum}, the first \emph{provably} Byzantine-tolerant aggregation rule for distributed Stochastic Gradient Descent (SGD). Krum guarantees the convergence of SGD even in a distributed setting where (asymptotically) up to half of the workers can be malicious adversaries trying to attack the learning system.

Official source

https://infoscience.epfl.ch/entities/publication/b31b7876-98db-42ed-87cd-3e79c8eeeab1

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Rachid Guerraoui, El Mahdi El Mhamdi, Peva François Blanchard, Julien Michel Stainer
2017
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/b31b7876-98db-42ed-87cd-3e79c8eeeab1

About this result

Related concepts (6)

Learning classifier system

Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling, classification, data mining, regression, function approximation, or game strategy).

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