Layerwise Geo-Distributed Computing between Cloud and IoT

In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed computing extends Cloud to the geographical verge of the network in the neighbo...

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Bibliographic Details
Published in:arXiv.org 2022-01
Main Authors: Kamo, Satoshi, Sheng, Yiqiang
Format: Article
Language:English
Subjects:
Artificial neural networks
Belief networks
Cloud computing
Computer networks
Deep learning
Distributed processing
Internet of Things
Machine learning
Online Access:Get full text
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Summary:In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed computing extends Cloud to the geographical verge of the network in the neighbor of IoT. The basic ideas of the proposal include a k-degree constraint and a layer-wise constraint. The k-degree constraint is defined such that the degree of each vertex on the h-th layer is exactly k(h) to extend the existing deep belief networks and control the communication cost. The layer-wise constraint is defined such that the layer-wise degrees are monotonically decreasing in positive direction to gradually reduce the dimension of data. We prove the k-degree layer-wise network is sparse, while a typical deep neural network is dense. In an evaluation on the M-distributed MNIST database, the proposal is superior to a state-of-the-art model in terms of communication cost and learning time with scalability.
ISSN:2331-8422