Optimization Algorithms for Distributed Machine Learning

1. Front Matter

   Pages i-xiii

   PDF

2. Distributed Optimization in Machine Learning

   • Gauri Joshi

   Pages 1-12

3. Calculus, Probability and Order Statistics Review

   • Gauri Joshi

   Pages 13-25

4. Convergence of SGD and Variance-Reduced Variants

   • Gauri Joshi

   Pages 27-38

5. Synchronous SGD and Straggler-Resilient Variants

   • Gauri Joshi

   Pages 39-49

6. Asynchronous SGD and Staleness-Reduced Variants

   • Gauri Joshi

   Pages 51-66

7. Local-Update and Overlap SGD

   • Gauri Joshi

   Pages 67-92

8. Quantized and Sparsified Distributed SGD

   • Gauri Joshi

   Pages 93-106

9. Decentralized SGD and Its Variants

   • Gauri Joshi

   Pages 107-121

10. Beyond Distributed Training in the Cloud

    • Gauri Joshi

    Pages 123-127

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.

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

• Carnegie Mellon University, Pittsburgh, USA

  Gauri Joshi

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).