High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers

arXiv:2307.13352v2 Announce Type: replace
Abstract: Robust distributed learning with Byzantine failures has attracted extensive research interests in recent years. However, most of existing methods suffer from curse of dimensionality, which is increasingly serious with the growing complexity of modern machine learning models. In this paper, we design a new method that is suitable for high dimensional problems, under arbitrary number of Byzantine attackers. The core of our design is a direct high dimensional semi-verified mean estimation method. Our idea is …

abstract arxiv complexity cs.cr cs.dc cs.lg design dimensionality distributed distributed learning gradient however machine machine learning machine learning models modern paper research robust type