Layerwise Geodistributed Computing Between Cloud And Iot
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**arXiv ID:** 2201.07215 **Authors:** Satoshi Kamo, Yiqiang Sheng **Published:** 2022-01-14T23:41:04Z **Abstract:** 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 lay...
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# Layerwise Geo-Distributed Computing between Cloud and IoT
**arXiv ID:** 2201.07215
**Authors:** Satoshi Kamo, Yiqiang Sheng
**Published:** 2022-01-14T23:41:04Z
**Abstract:**
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.
## Skill Description
This skill is generated from the arXiv paper: Layerwise Geo-Distributed Computing between Cloud and IoT (2201.07215).
## How to Use
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## References
- [arXiv:2201.07215](http://arxiv.org/abs/2201.07215v1)
