Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
arXiv:2405.02060 · doi:10.1109/ICCP56966.2022.10053975
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 data set.
7 pages, 9 figures, 1 table, ICCP Conference 2022