DLSTM: Distributed Long Short-Term Memory Neural Networks for the Internet of Things

Although the development of Internet of Things (IoT) provides a significant boost for the applications of deep learning algorithms, it is generally hard to fully implement the deep learning algorithms by IoT devices due to their limited calculation capacity. The problem could be alleviated by deploying the deep learning algorithms with edge computing. Herein, in this article, we propose a kind of distributed long short-term memory (DLSTM) neural networks and deploy them on the IoT environment to handle the large-scale spatiotemporal correlation regression tasks. Specifically, the presented DLSTM neural networks adopt the collaborative computing architecture with the terminals, edges and cloud, in order to realize the lightweight deep learning on the IoT devices and improve the learning efficiency. The generalization ability of LSTM neural networks is promoted through introducing the distributed memory cells to implement the information sharing between different edge servers and employing the attention mechanism in LSTM neural networks. Meanwhile, the deep fully connected networks are deployed among the cloud to extract the spatiotemporal correlations in the variety of data from different time and space regions, which enhances the transferability of LSTM neural networks. Numerical experiment shows that the proposed DLSTM neural networks reduce 36% model parameters size, 32% memory consumption among the cloud, and more than half of the prediction errors compared with traditional LSTM neural networks. Besides, the favourable transferability of the present DLSTM neural networks is also verified via numerical experiment.