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Optimization Algorithms for Distributed Machine Learning

Authors:

  • Discusses state-of-the-art algorithms that are at the core of the field of federated learning

  • Analyzes each algorithm based on its error versus iterations convergence, and the runtime spent per iteration

  • Provides insight into how the communication and synchronization protocol affects their practical performance

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xiii
  2. Local-Update and Overlap SGD

    • Gauri Joshi
    Pages 67-92
  3. Decentralized SGD and Its Variants

    • Gauri Joshi
    Pages 107-121
  4. Beyond Distributed Training in the Cloud

    • Gauri Joshi
    Pages 123-127

About this book

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.

Keywords

  • Distributed Machine Learning
  • Distributed Optimization
  • Optimization Algorithms
  • Stochastic Gradient Descent
  • Distributed SGD
  • Large-scale Machine Learning
  • Federated Learning

Authors and Affiliations

  • Carnegie Mellon University, Pittsburgh, USA

    Gauri Joshi

About the author

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).

Bibliographic Information

Buy it now

Buying options

eBook USD 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 44.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 19.99 USD 44.99
Discount applied Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access