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

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