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:在感觉系统中,车辆将数据上传到边缘服务器,这些服务器将培训车辆的数据更新本地模型,然后将结果归还车辆,以避免共享原始数据;然而,边缘的缓存队列有限,边端服务器和每辆车之间的通道是时间变化的。因此,选择适当数量的车辆以确保上载数据能够在边端服务器上保持稳定的缓存队列,同时最大限度地提高学习准确性,是一项很有希望的技术。此外,选择具有不同资源状况的车辆更新数据将影响培训所涉数据的总量,从而进一步影响模型准确性。我们在本文件中提议了一项车辆选择计划,在确保缓存队列稳定性的同时,最大限度地提高学习准确性,同时考虑到边端服务器覆盖的所有车辆的稳定性。通过模拟实验评估这一计划的执行情况,这表明我们拟议的计划可以比已知的基准计划做得更好。</s>