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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.<\/jats:p>","DOI":"10.1155\/2020\/6626471","type":"journal-article","created":{"date-parts":[[2020,11,22]],"date-time":"2020-11-22T01:50:09Z","timestamp":1606009809000},"page":"1-10","source":"Crossref","is-referenced-by-count":6,"title":["Fed-SCNN: A Federated Shallow-CNN Recognition Framework for Distracted Driving"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7118-771X","authenticated-orcid":true,"given":"Yaojie","family":"Wang","sequence":"first","affiliation":[{"name":"Counter-Terrorism Command Information Engineering Research Team, Engineering University of PAP, Xi\u2019an 710086, China"}]},{"given":"Xiaolong","family":"Cui","sequence":"additional","affiliation":[{"name":"Counter-Terrorism Command Information Engineering Research Team, Engineering University of PAP, Xi\u2019an 710086, China"}]},{"given":"Zhiqiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Counter-Terrorism Command Information Engineering Research Team, Engineering University of PAP, Xi\u2019an 710086, China"}]},{"given":"Bo","family":"Gan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trf.2020.08.003"},{"key":"2","article-title":"An examination of driver distraction as recorded in NHTSA databases","volume-title":"Traffic Safety Facts\u2014Research Note","author":"D. 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