Optimization Algorithms for Distributed Machine Learning (eBook)

Gauri Joshi (Autor)

eBook Download: PDF

2022
127 Seiten
Springer International Publishing (Verlag)
978-3-031-19067-4 (ISBN)

Infos

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.

Produkt-Details

• Erscheint lt. Verlag: 25.11.2022
• Reihe/Serie: Synthesis Lectures on Learning, Networks, and Algorithms
• Reihe/Serie: Synthesis Lectures on Learning, Networks, and Algorithms
• Zusatzinfo: XIII, 127 p. 40 illus., 38 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Schlagworte: Distributed Machine Learning • Distributed Optimization • Distributed SGD • federated learning • Large-scale Machine Learning • optimization algorithms • Stochastic Gradient descent
• ISBN-10: 3-031-19067-X / 303119067X
• ISBN-13: 978-3-031-19067-4 / 9783031190674