Synthesis Lectures on Learning, Networks, and Algorithms
Verlag:
Springer International Publishing
Erschienen:
25.11.2022
Seitenanzahl:
127
EAN:
9783031190674
Format:
eBook
Schutz:
Dig. Wass.
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Optimization Algorithms for Distributed Machine Learning
Gauri Joshi
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.
