In this paper, we show how Federated Learning (FL) can be applied to vehicular use-cases in which we seek to classify obstacles, irregularities and pavement types on roads. Our proposed framework utilizes FL and TabNet, a state-of-the-art neural network for tabular data. We are the first to demonstrate how TabNet can be integrated with FL. Moreover, we achieve a maximum test accuracy of 93.6%. Finally, we reason why FL is a suitable concept for this data set.

翻译：本文展示了如何将联邦学习（FL）应用于车辆场景中，该场景旨在对道路上的障碍物、不规则路面及路面类型进行分类。我们提出的框架采用了FL与TabNet（一种专为表格数据设计的最先进神经网络）。我们首次论证了如何将TabNet与FL进行集成。此外，我们实现了93.6%的最高测试准确率。最后，我们阐述了为何FL是适用于该数据集的概念。