A Machine Learning Solution for Distributed Environments and Edge Computing†
AbstractIn 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.
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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.
Penas-Noce J, Fontenla-Romero Ó, Guijarro-Berdiñas B. A Machine Learning Solution for Distributed Environments and Edge Computing. Proceedings. 2019; 21(1):47.Chicago/Turabian Style
Penas-Noce, Javier; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha. 2019. "A Machine Learning Solution for Distributed Environments and Edge Computing." Proceedings 21, no. 1: 47.
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