Xingrong Dong

Distributed Online Learning With Adversarial Participants In An Adversarial Environment

This paper studies distributed online learning under Byzantine attacks. The performance of an online learning algorithm is characterized by (adversarial) regret, and a sublinear bound is preferred. But we prove that, even with a class of state-of-the-art robust aggregation rules, in an adversarial e…