FedSup: A communication-efficient federated learning fatigue driving behaviors supervision approach

With the proliferation of smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving. Data sharing among vehicles can be used to optimize fatigue detection models and ensure driving safety. However, data privacy issues hinder the sharing process. Besides, due to the limitation of communication and computing resources, it is difficult to carry out training and data transmission on vehicles. To tackle these challenges, we propose FedSup, a communication-efficient federated learning method for fatigue driving behaviors supervision. Inspired by the resources allocation mechanism in edge intelligence, FedSup dynamically optimizes the sharing model with tailored client–edge–cloud architecture and reduces communication overhead by a Bayesian Convolutional Neural Network (BCNN) data selection strategy. To improve the sharing model optimize efficiency, we further propose an asynchronous parameters aggregation algorithm to automatically adjust the mixing weight of each edge model parameter. Extensive experiments demonstrate that the FedSup method is suitable for IoV scenarios and outperforms related federated learning methods in terms of communication overhead and model accuracy.