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

TL;DR

This paper demonstrates how federated learning combined with TabNet can effectively classify road obstacles and pavement types in vehicular scenarios, achieving high accuracy while preserving data privacy.

Contribution

First integration of TabNet with federated learning for tabular data in vehicular applications, showing promising accuracy and practical benefits.

Findings

Maximum test accuracy of 93.6% achieved

First demonstration of TabNet with federated learning

FL is suitable for vehicular tabular data

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.