  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.
