Tianqiang Huang

High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers

Adversarial attacks pose a major challenge to distributed learning systems, prompting the development of numerous robust learning methods. However, most existing approaches suffer from the curse of dimensionality, i.e. the error increases with the number of model parameters. In this paper, we make a