Summary of Federated Learning For Tabular Data Using Tabnet: a Vehicular Use-case, by William Lindskog and Christian Prehofer
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
by William Lindskog, Christian Prehofer
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework integrates Federated Learning (FL) and TabNet, a state-of-the-art neural network for tabular data, to classify obstacles, irregularities, and pavement types on roads. The framework demonstrates the application of FL in vehicular use-cases, achieving a maximum test accuracy of 93.6%. By leveraging FL and TabNet, the study shows that this approach is suitable for this specific dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how Federated Learning can be used to classify road types, obstacles, and irregularities. It uses a new combination of techniques, called TabNet, which helps with tabular data. This means we can get accurate results without sharing all the data. The paper shows that this approach works well and could help with other problems too. |
Keywords
» Artificial intelligence » Federated learning » Neural network
