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A Machine Learning Solution for Distributed Environments and Edge Computing

Universidade da Coruña, CITIC, Campus de Elviña s/n, 15071 A Coruña, Spain
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Presented at the 2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019.
Proceedings 2019, 21(1), 47; https://doi.org/10.3390/proceedings2019021047
Published: 9 August 2019
(This article belongs to the Proceedings of XoveTIC Conference)
PDF [147 KB, uploaded 9 August 2019]

Abstract

In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive and of reduced size. Moreover, they should operate in a distributed manner making use of edge computing capabilities while preserving local data privacy. The aim of this work is to provide a solution offering all these features by implementing the algorithm LANN-DSVD over a cluster of Raspberry Pi devices. In this system, every node first learns locally a one-layer neural network. Later on, they share the weights of these local networks to combine them into a global net that is finally used at every node. Results demonstrate the benefits of the proposed system.
Keywords: machine learning; distributed learning; artificial neural networks; Big Data; privacy- preserving; Internet of Things; edge computing; Raspberry; TensorFlow machine learning; distributed learning; artificial neural networks; Big Data; privacy- preserving; Internet of Things; edge computing; Raspberry; TensorFlow
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Penas-Noce, J.; Fontenla-Romero, Ó.; Guijarro-Berdiñas, B. A Machine Learning Solution for Distributed Environments and Edge Computing. Proceedings 2019, 21, 47.

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