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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.
Table des matières
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.
A propos de l'auteur
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).
