June 10, 2024, 4:46 a.m. | Grigory Malinovsky, Peter Richt\'arik, Samuel Horv\'ath, Eduard Gorbunov

cs.LG updates on arXiv.org arxiv.org

arXiv:2311.14127v2 Announce Type: replace
Abstract: Distributed learning has emerged as a leading paradigm for training large machine learning models. However, in real-world scenarios, participants may be unreliable or malicious, posing a significant challenge to the integrity and accuracy of the trained models. Byzantine fault tolerance mechanisms have been proposed to address these issues, but they often assume full participation from all clients, which is not always practical due to the unavailability of some clients or communication constraints. In our work, …

abstract accuracy arxiv challenge clip cs.ai cs.dc cs.lg differences distributed distributed learning gradient however integrity machine machine learning machine learning models math.oc paradigm replace robustness training type world

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