Phocas: dimensional Byzantine-resilient stochastic gradient descent

Xie, Cong, Koyejo, Oluwasanmi, Gupta, Indranil

arXiv.org Machine Learning 

We propose a novel robust aggregation rule for distributed synchronous Stochastic Gradient Descent (SGD) under a general Byzantine failure model. The attackers can arbitrarily manipulate the data transferred between the servers and the workers in the parameter server (PS) architecture. We prove the Byzantine resilience of the proposed aggregation rules. Empirical analysis shows that the proposed techniques outperform current approaches for realistic use cases and Byzantine attack scenarios.

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