Optimization Algorithms for Distributed Machine Learning

AvGauri Joshi

Häftad, Engelska, 2023

Del i serien Synthesis Lectures on Learning, Networks, and Algorithms

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2023-11-26
Mått:168 x 240 x 9 mm
Vikt:255 g
Format:Häftad
Språk:Engelska
Serie:Synthesis Lectures on Learning, Networks, and Algorithms
Antal sidor:127
Upplaga:23001
Förlag:Springer International Publishing AG
ISBN:9783031190698

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Matematisk statistik inom Naturvetenskap och teknik
Programmeringsböcker inom Data och IT

Mer om författaren

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).

Innehållsförteckning

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.