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Robust Decentralized Learning with Local Updates and Gradient Tracking
May 3, 2024, 4:52 a.m. | Sajjad Ghiasvand, Amirhossein Reisizadeh, Mahnoosh Alizadeh, Ramtin Pedarsani
cs.LG updates on arXiv.org arxiv.org
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
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