Optimization Algorithms for Distributed Machine Learning

Paperback Engels 2023 9783031190698
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Samenvatting

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.

Specificaties

ISBN13:9783031190698
Taal:Engels
Bindwijze:paperback
Uitgever:Springer International Publishing

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Inhoudsopgave

​Distributed Optimization in Machine Learning.-&nbsp;Calculus, Probability and Order Statistics Review.-&nbsp;Convergence of SGD and Variance-Reduced Variants.-&nbsp;Synchronous SGD and Straggler-Resilient Variants.-&nbsp;Asynchronous SGD and Staleness-Reduced Variants.-&nbsp;Local-update and Overlap SGD.-&nbsp;Quantized and Sparsified Distributed SGD.-<div>Decentralized SGD and its Variants.<br></div>

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        Optimization Algorithms for Distributed Machine Learning