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.

翻译：联邦边缘学习（FEEL）技术被视为车载网络中既能降低计算负载又能保护用户隐私的前沿技术。在FEEL系统中，车辆将数据上传至边缘服务器，由服务器训练车辆数据以更新本地模型，再将结果返回车辆，从而避免共享原始数据。然而，边缘缓存队列容量有限，且边缘服务器与各车辆间的信道具有时变特性。因此，如何选择合适数量的车辆，确保上传数据能维持边缘服务器的缓存队列稳定，同时最大化学习精度具有挑战性。此外，选择不同资源状态的车辆更新数据会影响参与训练的数据总量，进而影响模型精度。本文提出一种车辆选择方案，该方案在考虑边缘服务器覆盖范围内所有车辆状态的基础上，能够在保证缓存队列稳定性的同时最大化学习精度。仿真实验评估表明，该方案性能优于现有基准方案。