The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility characteristics of vehicles, it is necessary to cache the user data in advance and learn the most popular and interesting contents for vehicular users. Since user data usually contains privacy information, users are reluctant to share their data with others. To solve this problem, traditional federated learning (FL) needs to update the global model synchronously through aggregating all users' local models to protect users' privacy. However, vehicles may frequently drive out of the coverage area of the VEC before they achieve their local model trainings and thus the local models cannot be uploaded as expected, which would reduce the accuracy of the global model. In addition, the caching capacity of the local RSU is limited and the popular contents are diverse, thus the size of the predicted popular contents usually exceeds the cache capacity of the local RSU. Hence, the VEC should cache the predicted popular contents in different RSUs while considering the content transmission delay. In this paper, we consider the mobility of vehicles and propose a cooperative Caching scheme in the VEC based on Asynchronous Federated and deep Reinforcement learning (CAFR). We first consider the mobility of vehicles and propose an asynchronous FL algorithm to obtain an accurate global model, and then propose an algorithm to predict the popular contents based on the global model. In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay. Extensive experimental results have demonstrated that the CAFR scheme outperforms other baseline caching schemes.

翻译：电视边缘计算(Vec)可以在网络边缘的不同区域SU中隐藏内容,用于支持实时车辆应用程序。在VEC,由于车辆移动性特点高,有必要预先隐藏用户数据,并为车辆用户学习最受欢迎和最有趣的内容。由于用户数据通常包含隐私信息,用户不愿意与其他用户分享数据。为解决这一问题,传统联邦学习(FL)需要同步更新全球模型,方法是汇集所有用户的当地深层模型,以保护用户隐私。然而,车辆可能经常在实现其当地模式培训之前就离开VEC的覆盖区域,因此无法如预期的那样上传当地模式,这将降低全球模式的准确性。此外,由于当地RSU的缩放能力有限,用户不愿意与其他用户分享数据。因此,预测的大众内容的规模通常超过当地RSU的缓存模型。因此,VEC应该将预测的大众数据存储在不同的区域SUSU,同时考虑内容传输延迟。在本文中,我们考虑以最佳合作性成本成本传输为主的系统,我们考虑SEA的移动计划,我们考虑以合作性车辆的递增量为主,我们提议一个基于CFSU的流流到另一个系统。