# Federated Learning for Drowsiness Detection in Connected Vehicles

arXiv: https://arxiv.org/abs/2405.03311 · HTML: https://mlsanity.com/paper/2405.03311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition](https://mlsanity.com/paper/2010.11757/) (2020)
- [Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy](https://mlsanity.com/paper/1810.01835/) (2018)
- [Survey and synthesis of state of the art in driver monitoring](https://mlsanity.com/paper/2110.00472/) (2021)
- [Driver Drowsiness Detection Using Ensemble Convolutional Neural Networks on YawDD](https://mlsanity.com/paper/2112.10298/) (2021)
- [Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case](https://mlsanity.com/paper/2405.02060/) (2024)

## Abstract (arXiv)

Ensuring driver readiness poses challenges, yet driver monitoring systems can assist in determining the driver's state. By observing visual cues, such systems recognize various behaviors and associate them with specific conditions. For instance, yawning or eye blinking can indicate driver drowsiness. Consequently, an abundance of distributed data is generated for driver monitoring. Employing machine learning techniques, such as driver drowsiness detection, presents a potential solution. However, transmitting the data to a central machine for model training is impractical due to the large data size and privacy concerns. Conversely, training on a single vehicle would limit the available data and likely result in inferior performance. To address these issues, we propose a federated learning framework for drowsiness detection within a vehicular network, leveraging the YawDD dataset. Our approach achieves an accuracy of 99.2%, demonstrating its promise and comparability to conventional deep learning techniques. Lastly, we show how our model scales using various number of federated clients