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Machine Learning with Adversaries: Byzantine Tolerant ...

Machine Learning with Adversaries: Byzantine Tolerant Gradient DescentPeva BlanchardEPFL, Mahdi El Mhamdi EPFL, GuerraouiEPFL, StainerEPFL, study the resilience to Byzantine failures of distributed implementations ofStochastic Gradient Descent (SGD). So far, distributed Machine Learning frame-works have largely ignored the possibility of failures, especially arbitrary ( , Byzantine ) ones. Causes of failures include software bugs, network asynchrony,biases in local datasets, as well as attackers trying to compromise the entire a set ofnworkers, up tofbeing Byzantine , we ask how resilient canSGD be, without limiting the dimension, nor the size of the parameter space.

Stochastic Gradient Descent (SGD). So far, distributed machine learning frame-works have largely ignored the possibility of failures, especially arbitrary (i.e., Byzantine) ones. Causes of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system.

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