EAN: 9783031190698

SYNTHESIS LECTURES ON LEARNING NETWORKS AND ALG...

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.

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