Abstract: In consideration of the characteristics of multi-dimensional resource consumption fluctuating with time in the Internet of Vehicles system and users’ demands for efficient computing services and data privacy and security,this paper proposes a me-thod of multi-dimensional resource allocation for Internet of Vehicles based on federated learning.On the one hand,the allocation of computing,cache and bandwidth resources is considered comprehensively to ensure the completion rate of computing tasks and avoid the redundant allocation of multidimensional resources.For this purpose,a deep learning algorithm is designed to predict the consumption of various resources through the data collected by edge servers.On the other hand,considering the data island problem caused by users’ data privacy and security requirements,federated learning architecture is adopted to obtain a neural network model with better generalization.The proposed algorithm can not only adjust the allocation of multi-dimensional resources over time,but also meet the resource requirements that change over time,and ensure the efficient completion of computing tasks in the Internet of Vehicles system.Experimental results show that the algorithm has the characteristics of fast convergence and good model generalization,and can complete the aggregation of federated learning with fewer communication rounds.

Key words: Vehicle networks, Federated learning, Multi-dimensional resource allocation, Computational migration, Machine lear-ning