A simplified convergence theory for Byzantine resilient stochastic gradient descent

A simplified convergence theory for Byzantine resilient stochastic gradient descent

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In distributed learning, a central server trains a model swisse high strength magnesium powder berry according to updates provided by nodes holding local data samples.In the presence of one or more malicious servers sending incorrect information (a Byzantine adversary), standard algorithms for model training such as stochastic gradient descent (SGD) fail to converge.In this paper, we present a simplified convergence theory for the generic Byzantine Resilient SGD method originally proposed by Blanchard et al.

(2017) [3].Compared to the existing analysis, we shown convergence to a stationary point in expectation under standard assumptions on valhalla axys the (possibly nonconvex) objective function and flexible assumptions on the stochastic gradients.