FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework
This addresses privacy and efficiency issues in intelligent fatigue detection for drivers, but it is incremental as it builds on existing federated learning techniques.
The paper tackles privacy and communication costs in fatigue driving detection by proposing FedSup, a client-edge-cloud framework that uses federated learning and Bayesian CNN approximation, achieving improved performance over mainstream methods in IoV scenarios.
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.