论文标题
用于边缘辅助车辆网络的异步联合学习
Asynchronous Federated Learning for Edge-assisted Vehicular Networks
论文作者
论文摘要
车辆网络使车辆能够通过培训数据支持实时车辆应用。由于计算能力有限，车辆通常将数据传输到网络边缘的路边单元（RSU）以处理数据。但是，由于隐私问题，车辆通常不愿意相互共享数据。对于传统的联合学习（FL），车辆在本地训练数据以获取本地模型，然后将本地模型上传到RSU以更新全局模型，因此可以通过共享模型参数而不是数据来保护数据隐私。传统的FL同步更新全局模型，即RSU需要等待所有车辆上传其模型以进行全局模型更新。但是，车辆通常可能会在RSU通过训练获得本地模型之前脱离覆盖范围，从而降低了全球模型的准确性。有必要提出一个异步联合学习（AFL）来解决此问题，其中RSU一旦从车辆中收到本地模型就会更新全球模型。但是，数据量，计算能力和车辆迁移率可能会影响全球模型的准确性。在本文中，我们共同考虑数据，计算功能和车辆迁移率以设计AFL方案以提高全球模型的准确性。广泛的仿真实验表明，我们的方案的表现优于FL方案
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
