Fed-SCNN: A Federated Shallow-CNN Recognition Framework for Distracted Driving
Joint Authors
Gao, Zhiqiang
Cui, Xiaolong
Wang, Yaojie
Gan, Bo
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-21
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Although distracted driving recognition is of great significance to traffic safety, drivers are reluctant to provide their own personalized driving data to machine learning because of privacy protection.
How to improve the accuracy of distracted driving recognition on the basis of ensuring privacy protection? To address the issue, we proposed the federated shallow-CNN recognition framework (Fed-SCNN).
Firstly, a hybrid model is established on the user-side through DNN and shallow-CNN, which recognizes the data of the in-vehicle images and uploads the encrypted parameters to the cloud.
Secondly, the cloud server performs federated learning on major parameters through DNN to build a global cloud model.
Finally, The DNN is updated in the user-side to further optimize the hybrid model.
The above three steps are cycled to iterate the local hybrid model continuously.
The Fed-SCNN framework is a dynamic learning process that addresses the two major issues of data isolation and privacy protection.
Compared with the existing machine learning method, Fed-SCNN has great advantages in accuracy, safety, and efficiency and has important application value in the field of safe driving.
American Psychological Association (APA)
Wang, Yaojie& Cui, Xiaolong& Gao, Zhiqiang& Gan, Bo. 2020. Fed-SCNN: A Federated Shallow-CNN Recognition Framework for Distracted Driving. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1208482
Modern Language Association (MLA)
Wang, Yaojie…[et al.]. Fed-SCNN: A Federated Shallow-CNN Recognition Framework for Distracted Driving. Security and Communication Networks No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1208482
American Medical Association (AMA)
Wang, Yaojie& Cui, Xiaolong& Gao, Zhiqiang& Gan, Bo. Fed-SCNN: A Federated Shallow-CNN Recognition Framework for Distracted Driving. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1208482
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208482