Edge Caching Strategy of Internet of Vehicles Based on Federated and Reinforcement Learning
In order to solve the problem that the traditional content popularity prediction method in the Internet of Vehicles cannot accurately capture the vehicle request characteristics and leads to the low cache hit rate,an edge collaborative caching strategy based on federated learning and reinforcement learning is proposed.This strategy pre-caches content with a higher probability of vehicle requests in other vehicles or roadside units to improve the cache hit ratio and reduce the average content acquisition delay.The federated learning method is used to train and predict the content popularity using private data distributed across multiple vehicles,and then the reinforcement learning algorithm is used to solve the objective function to obtain the best cache location for the popular content.The results show that the proposed strategy is better than other caching strategies in terms of cache hit ratio and average content acquisition delay,which effectively improves the performance of the edge cache of the Internet of Vehicles.
Intelligent transportationEdge cachingInternet of VehiclesFederated learningReinforcement learning
万方数据
维普
