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.Comment: This paper has been submitted to IEEE Journal of Selected Topics in
Signal Processin