Optimization Algorithms for Distributed Machine Learning

Overview

This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed.

Introduction to Stochastic Gradient Descent

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.

Algorithms for Scalability and Efficiency

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.

Analysis of Algorithms

For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration.

Trade-offs

The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.