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Coded Decentralized Learning with Gradient Descent for Big Data Analytics
RISE - Research Institutes of Sweden, ICT, SICS.
KTH Royal Institute of Technology, Sweden.
2019 (English)In: IEEE Communications Letters, ISSN 2373-7891Article in journal (Refereed) Epub ahead of print
Abstract [en]

Machine learning is an effective technique for big data analytics. We focus on the study of big data analytics with decentralized learning in large-scale networks. Fountain codes are applied to the decentralized learning process to reduce communication load for exchanging intermediate learning parameters among fog nodes. Two scenarios, i.e., disjoint datasets and overlapping datasets, are analyzed. Comparison results show that communication load can be reduced significantly by the Fountain-based scheme for large-scale networks, especially when the quality of communication links is relatively bad and/or the number of fog nodes is large.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Big Data, Encoding, Decoding, 1/f noise, Task analysis, Generators, Machine learning, decentralized learning, gradient descent, Fountain codes, communication load
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-42606DOI: 10.1109/LCOMM.2019.2930513OAI: oai:DiVA.org:ri-42606DiVA, id: diva2:1384645
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-10Bibliographically approved

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CiteExportLink to record
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