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Title: A survey on deploying mobile deep learning applications : a systemic and technical perspective
Authors: Wang, Y
Wang, J
Zhang, W
Zhan, Y 
Guo, S 
Zheng, Q
Wang, X
Issue Date: Feb-2022
Source: Digital communications and networks, Feb. 2022, v. 8, no. 1, p. 1-17
Abstract: With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.
Keywords: Deep learning
Distributed caching
Distributed offloading
Mobile computing
Publisher: Ke Ai Publishing Communications Ltd.
Journal: Digital communications and networks 
ISSN: 2468-5925
EISSN: 2352-8648
DOI: 10.1016/j.dcan.2021.06.001
Rights: © 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (
The following publication Wang, Y., Wang, J., Zhang, W., Zhan, Y., Guo, S., Zheng, Q., & Wang, X. (2022). A survey on deploying mobile deep learning applications: A systemic and technical perspective. Digital Communications and Networks, 8(1), 1-17 is available at
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