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Coded Decentralized Learning With Gradient Descent for Big Data Analytics
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DOI：10.1109/LCOMM.2019.2930513.png)
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摘要
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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.
Keyword:
Big Data
Encoding
Decoding
1
f noise
Task analysis
Generators
Machine learning
Big data
decentralized learning
gradient descent
Fountain codes
communication load
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4.4
论文数:
1.3W
被引数:
2.2W
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