Optimization Algorithms for Distributed Machine Learning

Joshi, Gauri

ISBN 10: 3031190688 / ISBN 13: 9783031190681

Published by Springer 2022-11, 2022

About this Item

Seller Inventory # 6666-IUK-9783031190681

Bibliographic Details

• Title: Optimization Algorithms for Distributed Machine Learning
• Publisher: Springer 2022-11
• Publication Date: 2022
• Language: English
• ISBN 10: 3031190688
• ISBN 13: 9783031190681
• Binding: PF
• Condition: New

About this title

Synopsis

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.