Federated Learning With Data Reinforcement for Internet of Vehicles

Internet of Vehicles (IoV) is a typical extension of Internet of Things (IoT). Specifically, Federated Learning (FL) is capable of alleviating the knowledge sharing and privacy protection problems of IoV, and further enhancing the driving experience and service quality. However, dueto the scanty data results of new environment and the high-cost of expert data-labeling, posing an imminent challenge of how to reinforcement the vehicles’ data. In this letter, a data reinforcement mechanism is proposed to utilize the vehicles’ unlabeled dataset sufficiently and enhance the vehicle collaboration ultimately. Particularly, the dataset distribution characteristics of vehicles’ datasets are calculated to measure the dataset similarity. Furthermore, the labeling models are distributed to vehicles to empower the unlabeled data with the assistance of dataset distribution characteristics. Experimental results show that the proposed data reinforcement mechanism is capable of labeling the unlabeled data accurately and improving the performance of FL.