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In this paper, we propose a novel architecture for a deep learning system,named k-degree layer-wise network, to realize efficient geo-distributedcomputing between Cloud and Internet of Things (IoT). The geo-distributedcomputing extends Cloud to the geographical verge of the network in theneighbor of IoT. The basic ideas of the proposal include a k-degree constraintand a layer-wise constraint. The k-degree constraint is defined such that thedegree of each vertex on the h-th layer is exactly k(h) to extend the existingdeep belief networks and control the communication cost. The layer-wiseconstraint is defined such that the layer-wise degrees are monotonicallydecreasing in positive direction to gradually reduce the dimension of data. Weprove the k-degree layer-wise network is sparse, while a typical deep neuralnetwork is dense. In an evaluation on the M-distributed MNIST database, theproposal is superior to a state-of-the-art model in terms of communication costand learning time with scalability.

Layerwise Geo-Distributed Computing between Cloud and IoT