Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 37 references that are in the map. First posted on arXiv 3 May 2024.
Builds on
13 of 37 references.
References · 13
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
- 2019TabNet: Attentive Interpretable Tabular Learningcited by 640
- 2020The Future of Digital Health with Federated Learningcited by 625
- 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
- 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
- 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
- 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
- 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
- 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
- 2019Practical Federated Gradient Boosting Decision Treescited by 53
- 2019Federated Forestcited by 49
- 2020The Canonical Interval Forest (CIF) Classifier for Time Series Classificationcited by 48
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