  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
