Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916234299179008 |
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| author | Lindskog, William Prehofer, Christian |
| author_facet | Lindskog, William Prehofer, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02060 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case Lindskog, William Prehofer, Christian Machine Learning 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. |
| title | Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.02060 |