Optimization Algorithms for Distributed Machine Learning

Inbunden, Engelska, 2022

Av Gauri Joshi

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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.

Produktinformation

Utgivningsdatum2022-11-26
Mått168 x 240 x 14 mm
Vikt430 g
FormatInbunden
SpråkEngelska
SerieSynthesis Lectures on Learning, Networks, and Algorithms
Antal sidor127
FörlagSpringer International Publishing AG
ISBN9783031190667

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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).