Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits

Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits

We consider the distributed stochastic gradient descent problem, where a main node distributes gradient calculations among <i>n</i> workers. By assigning tasks to all workers and waiting only for the <i>k</i> fastest ones, the main node can trade off the algorithm’s error wit...

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Bibliographic Details
Main Authors: Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Deniz Gündüz
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Entropy
Subjects:
distributed machine learning
multi-armed bandits
stochastic gradient descent
straggler mitigation
Online Access:https://www.mdpi.com/1099-4300/27/5/541
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