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This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver\u2019s drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver\u2019s privacy and improves communication efficiency. <\/jats:p>","DOI":"10.1142\/s0129065723500090","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T13:27:27Z","timestamp":1670246847000},"source":"Crossref","is-referenced-by-count":16,"title":["Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network"],"prefix":"10.1142","volume":"33","author":[{"given":"Xue","family":"Qin","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, P. R. 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