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With the proliferation of edge smart devices and the Internet of Vehicles(IoV) technologies, intelligent fatigue detection has become one of themost-used methods in our daily driving. To improve the performance of thedetection model, a series of techniques have been developed. However, existingwork still leaves much to be desired, such as privacy disclosure andcommunication cost. To address these issues, we propose FedSup, aclient-edge-cloud framework for privacy and efficient fatigue detection.Inspired by the federated learning technique, FedSup intelligently utilizes thecollaboration between client, edge, and cloud server to realizing dynamic modeloptimization while protecting edge data privacy. Moreover, to reduce theunnecessary system communication overhead, we further propose a Bayesianconvolutional neural network (BCNN) approximation strategy on the clients andan uncertainty weighted aggregation algorithm on the cloud to enhance thecentral model training efficiency. Extensive experiments demonstrate that theFedSup framework is suitable for IoV scenarios and outperforms other mainstreammethods.

FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework