Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case

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

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Cited by in corpus (3)

• A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks
• Federated Learning for Drowsiness Detection in Connected Vehicles
• Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data