Continuous Deep Learning Based on Knowledge Transfer in Edge Computing

Edge computing enables real-time intelligent services to be provided near the environment where the data is generated. However, providing intelligent services requires sufficient computing ability that is limited by edge devices. Knowledge transfer is an approach that transfers the trained model from an edge device to another edge device. In this paper, we propose a continuous deep-learning approach based on knowledge transfer to reduce the training epochs of a single-edge device without sharing the data in the edge computing environment. For training deep learning continuously in the network edge, each edge device includes a deep learning model and local dataset to fine-tune the reached model. The fine-tuning is completed, then, the updated model is transferred to the next edge device. For experimenting with the proposed approach, edge computing is comprised of multiple edge devices that are emulated by the virtual machines to operate the deep learning model and simulate the network communication. The deep learning model is developed for classification based on Convolutional Neural Network (CNN). As expected, the prediction accuracy is improved in multiple iterations of transferring in the edge computing environment.