Optimization Algorithms for Distributed Machine Learning

Gauri Joshi (Autor)

Buch | Hardcover

XIII, 127 Seiten

2022 | 1st ed. 2023
Springer International Publishing (Verlag)
978-3-031-19066-7 (ISBN)

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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.

Gauri Joshi, Ph.D., is an associate professor in the ECE department at Carnegie Mellon University. Dr. Joshi completed her Ph.D. from MIT EECS. Her current research is on designing algorithms for federated learning, distributed optimization, and parallel computing. Her awards and honors include being named as one of MIT Technology Review's 35 Innovators under 35 (2022), the NSF CAREER Award (2021), the ACM SIGMETRICS Best Paper Award (2020), Best Thesis Prize in Computer science at MIT (2012), and Institute Gold Medal of IIT Bombay (2010).

Distributed Optimization in Machine Learning.- Calculus, Probability and Order Statistics Review.- Convergence of SGD and Variance-Reduced Variants.- Synchronous SGD and Straggler-Resilient Variants.- Asynchronous SGD and Staleness-Reduced Variants.- Local-update and Overlap SGD.- Quantized and Sparsified Distributed SGD.-Decentralized SGD and its Variants.

Erscheinungsdatum	27.11.2022
Reihe/Serie	Synthesis Lectures on Learning, Networks, and Algorithms
Zusatzinfo	XIII, 127 p. 40 illus., 38 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	168 x 240 mm
Gewicht	380 g
Themenwelt	Informatik ► Theorie / Studium ► Algorithmen
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Mathematik ► Analysis
	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	3-031-19066-1 / 3031190661
ISBN-13	978-3-031-19066-7 / 9783031190667
Zustand	Neuware