3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning

arXiv: Learning, Volume abs/1802.07389, 2018.

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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

The performance and efficiency of distributed machine learning (ML) depends significantly on how long it takes for nodes to exchange state changes. Overly-aggressive attempts to reduce communication often sacrifice final model accuracy and necessitate additional ML techniques to compensate for this loss, limiting their generality. Some at...More

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