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- W4322731621 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." @default.
- W4322731621 created "2023-03-03" @default.
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- W4322731621 date "2022-09-22" @default.
- W4322731621 modified "2023-10-14" @default.
- W4322731621 title "Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case" @default.
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- W4322731621 doi "https://doi.org/10.1109/iccp56966.2022.10053975" @default.
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