Robust Decentralized Learning with Local Updates and Gradient Tracking

arXiv:2405.00965v1 Announce Type: new
Abstract: As distributed learning applications such as Federated Learning, the Internet of Things (IoT), and Edge Computing grow, it is critical to address the shortcomings of such technologies from a theoretical perspective. As an abstraction, we consider decentralized learning over a network of communicating clients or nodes and tackle two major challenges: data heterogeneity and adversarial robustness. We propose a decentralized minimax optimization method that employs two important modules: local updates and gradient tracking. Minimax optimization …

abstract abstraction applications arxiv computing cs.dc cs.lg decentralized distributed distributed learning edge edge computing federated learning gradient internet internet of things iot network nodes perspective robust technologies tracking type updates