Fed-SCNN: A Federated Shallow-CNN Recognition Framework for Distracted Driving. (21st November 2020)

• Record Type: Journal Article
• Authors: Wang, Yaojie; Cui, Xiaolong; Gao, Zhiqiang; Gan, Bo
• Other Names: Zhou Nanrun Academic Editor.
• Is Part Of: Security and communication networks. Volume 2020(2020)
• Publication Date: 2020-11-21
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2020/6626471
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : 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.