Federation-Based Deep Reinforcement Learning Cooperative Cache in Vehicular Edge Networks

With the emergence of a large number of computing resource-intensive applications and a variety of content delivery services, data in Internet of Vehicles (IoV) is exploding. In order to improve the service performance of IoV, Vehicle Edge Computing (VEC) accelerates the response process of content requests and reduces the backhaul burden of the base station by caching content at the nodes of the edge network. However, the existing caching strategies are usually affected by high computing and communication overhead, and can not well capture the dynamic changes and content popularity of the vehicle network. In order to solve these problems, we design a novel Cooperative Caching scheme by using Mobility Prediction and Consistent Hash for Federated Learning (called CMCF), which integrates mobility prediction and consistent hashing into the content caching scheme, and uses the federated learning framework to optimize the cached content, then we use deep reinforcement learning algorithm to develop the optimal cooperative caching policy to reduce the average delay of content transmission. Extensive simulation results prove the superiority of our method. Compared with other advanced caching schemes, CMCF can increase the cache hit rate by 8.7% and reduce the average content delivery delay by 17.8%.