Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
<|reference_start|>Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case: 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.<|reference_end|>
@article{lindskog2024federated,
    title={Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case},
    author={William Lindskog and Christian Prehofer},
    journal={arXiv preprint arXiv:2405.02060},
    year={2024},
    doi={10.1109/ICCP56966.2022.10053975},
    archivePrefix={arXiv},
    eprint={2405.02060},
    primaryClass={cs.LG}
}