  Federated edge learning (FEEL) technology for vehicular networks is
considered as a promising technology to reduce the computation workload while
keeping the privacy of users. In the FEEL system, vehicles upload data to the
edge servers, which train the vehicles' data to update local models and then
return the result to vehicles to avoid sharing the original data. However, the
cache queue in the edge is limited and the channel between edge server and each
vehicle is time-varying. Thus, it is challenging to select a suitable number of
vehicles to ensure that the uploaded data can keep a stable cache queue in edge
server while maximizing the learning accuracy. Moreover, selecting vehicles
with different resource statuses to update data will affect the total amount of
data involved in training, which further affects the model accuracy. In this
paper, we propose a vehicle selection scheme, which maximizes the learning
accuracy while ensuring the stability of the cache queue, where the statuses of
all the vehicles in the coverage of edge server are taken into account. The
performance of this scheme is evaluated through simulation experiments, which
indicates that our proposed scheme can perform better than the known benchmark
scheme.
