000			01443 a2200217 4500
003			OSt
005			20250910123601.0
008			250910b |||||||| |||| 00| 0 eng d
020			_a9783031190667
082			_a006.31 _bJ837o
100			_aJoshi, Gauri
245			_aOptimization algorithms for distributed machine learning _cGauri Joshi
260			_bSpringer _c2023 _aSwitzerland
300			_axiii, 127p
440			_aSynthesis lectures on learning, networks, and algorithms
490			_a/ edited by Lei Ying
520			_aThis 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.
650			_aComputer algorithms
650			_aMachine learning
942			_cREF
999			_c567635 _d567635