Speeding Up Distributed Machine Learning Using Codes

Codes are widely used in many engineering applications to offer robustness against noise . In large-scale systems, there are several types of noise that can affect the performance of distributed machine learning algorithms-straggler nodes, system failures, or communication bottlenecks-but there has...

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Veröffentlicht in:	IEEE transactions on information theory Jg. 64; H. 3; S. 1514 - 1529
Hauptverfasser:	Lee, Kangwook, Lam, Maximilian, Pedarsani, Ramtin, Papailiopoulos, Dimitris, Ramchandran, Kannan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithms Artificial intelligence channel coding Codes Communication Communications systems Computer networks distributed computing Distributed databases Encoding Machine learning Machine learning algorithms Machine tools Multicast communication Multicasting Multiplication Multiplication & division Robustness Runtime System failures
ISSN:	0018-9448, 1557-9654
Online-Zugang:	Volltext
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