Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case

arXiv:2405.02060v1 Announce Type: new
Abstract: 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 …

abstract art arxiv case cases cs.lg data federated learning framework network neural network obstacles paper roads seek show state tabular tabular data type types