As a promising distributed learning paradigm, Federated Learning (FL) is expected to meet the ever-increasing needs of Machine Learning (ML) based applications in Intelligent Transportation Systems (ITS). It is a powerful tool that processes the large amount of on-board data while preserving its privacy by locally learning the models. However, training and transmitting the model parameters in vehicular networks consume significant resources and time, which is not suitable for applications with strict real-time requirements. Moreover, the quality of the data, the mobility of the participating vehicles, as well as their heterogeneous capabilities, can impact the performance of FL process, bringing to the forefront the optimization of the data selection and the clients resources. In this paper, we propose CRAS-FL, a Clustered Resource-Aware Scheme for FL in Vehicular Networks. The proposed approach bypasses (1) communication bottlenecks by forming groups of vehicles, where the Cluster Head (CH) is responsible of handling the communication and (2) computation bottlenecks by introducing an offloading strategy, where the availability of the extra resources on some vehicles is leveraged. Particularly, CRAS-FL implements a CH election Algorithm, where the bandwidth, stability, computational resources, and vehicles topology are considered in order to ensure reliable communication and cluster stability. Moreover, the offloading strategy studies the quality of the models and the resources of the clients, and accordingly allows computational offloading among the group peers. The conducted experiments show how the proposed scheme outperforms the current approaches in the literature by (1) reducing the communication overhead, (2) targeting more training data, and (3) reducing the clusters response time.

中文翻译：

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CRAS-FL：车辆网络中联邦学习的集群资源感知方案

作为一种有前途的分布式学习范式，联邦学习（FL）有望满足智能交通系统（ITS）中基于机器学习（ML）的应用不断增长的需求。它是一个强大的工具，可以处理大量机载数据，同时通过本地学习模型来保护其隐私。然而，车载网络中模型参数的训练和传输消耗大量资源和时间，不适合实时性要求严格的应用。此外，数据的质量、参与车辆的移动性及其异构能力都会影响 FL 流程的性能，从而使数据选择和客户资源的优化成为最重要的因素。在本文中，我们提出了 CRAS-FL，一种用于车载网络中 FL 的集群资源感知方案。所提出的方法通过形成车辆组来绕过（1）通信瓶颈，其中簇头（CH）负责处理通信，以及（2）通过引入卸载策略来绕过计算瓶颈，其中某些车辆上的额外资源的可用性是有杠杆的。特别是，CRAS-FL实现了CH选举算法，其中考虑了带宽、稳定性、计算资源和车辆拓扑，以确保可靠的通信和集群稳定性。此外，卸载策略研究模型的质量和客户端的资源，并相应地允许组对等点之间的计算卸载。进行的实验表明，所提出的方案如何通过以下方式优于文献中的当前方法：（1）减少通信开销，（2）瞄准更多训练数据，以及（3）减少集群响应时间。