Vehicular networks enable vehicles support real-time vehicular applications through training data. Due to the limited computing capability, vehicles usually transmit data to a road side unit (RSU) at the network edge to process data. However, vehicles are usually reluctant to share data with each other due to the privacy issue. For the traditional federated learning (FL), vehicles train the data locally to obtain a local model and then upload the local model to the RSU to update the global model, thus the data privacy can be protected through sharing model parameters instead of data. The traditional FL updates the global model synchronously, i.e., the RSU needs to wait for all vehicles to upload their models for the global model updating. However, vehicles may usually drive out of the coverage of the RSU before they obtain their local models through training, which reduces the accuracy of the global model. It is necessary to propose an asynchronous federated learning (AFL) to solve this problem, where the RSU updates the global model once it receives a local model from a vehicle. However, the amount of data, computing capability and vehicle mobility may affect the accuracy of the global model. In this paper, we jointly consider the amount of data, computing capability and vehicle mobility to design an AFL scheme to improve the accuracy of the global model. Extensive simulation experiments have demonstrated that our scheme outperforms the FL scheme

翻译：摘要：车载网络通过训练数据支持车辆实现实时车载应用。由于计算能力有限，车辆通常将数据传输至网络边缘的路侧单元（RSU）进行处理。然而，因隐私问题，车辆通常不愿彼此共享数据。传统联邦学习（FL）中，车辆在本地训练数据以获取局部模型，并将局部模型上传至RSU以更新全局模型，从而通过共享模型参数而非数据来保护数据隐私。传统FL采用同步方式更新全局模型，即RSU需等待所有车辆上传模型后方能进行全局模型更新。但车辆可能在完成训练获取局部模型前驶离RSU覆盖范围，导致全局模型准确率下降。为此，需要提出异步联邦学习（AFL）以解决该问题：当RSU从任意车辆接收局部模型时，立即更新全局模型。然而，数据量、计算能力及车辆移动性均可能影响全局模型准确率。本文联合考虑数据量、计算能力及车辆移动性，设计了一种AFL方案以提升全局模型准确率。大量仿真实验表明，本方案优于传统联邦学习方案。