Joshi

Optimization Algorithms for Distributed Machine Learning

Springer

ISBN 978-3-031-19069-8

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Bibliografische Daten

Fachbuch

Buch. Softcover

2023

2 s/w-Abbildungen, 38 Farbabbildungen.

In englischer Sprache

Umfang: xiii, 127 S.

Format (B x L): 16,8 x 24 cm

Gewicht: 255

Verlag: Springer

ISBN: 978-3-031-19069-8

Weiterführende bibliografische Daten

Produktbeschreibung

This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.

Autorinnen und Autoren

Kundeninformationen

Discusses state-of-the-art algorithms that are at the core of the field of federated learning Analyzes each algorithm based on its error versus iterations convergence, and the runtime spent per iteration Provides insight into how the communication and synchronization protocol affects their practical performance

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