Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
This work addresses data privacy and decentralization challenges in vehicular systems, but it is incremental as it combines existing methods (FL and TabNet) for a specific use-case.
The paper tackled the problem of classifying road conditions like obstacles and pavement types in vehicular applications by applying Federated Learning (FL) with TabNet, achieving a maximum test accuracy of 93.6%.
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.