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

翻译：确保驾驶员状态的警觉性是一项挑战，而驾驶员监控系统有助于判断驾驶员状态。通过观察视觉线索，此类系统可识别各种行为并将其与特定状态相关联。例如，打哈欠或眨眼可表明驾驶员疲劳。因此，驾驶员监控会生成大量分布式数据。采用机器学习技术（如驾驶员瞌睡检测）提供了一种潜在解决方案。然而，将数据传输至中心机进行模型训练因数据规模庞大及隐私问题而不可行。反之，在单一车辆上进行训练将限制可用数据量，并可能导致性能较差。针对这些问题，我们提出了一种面向车辆网络的瞌睡检测联邦学习框架，并利用YawDD数据集进行验证。该方法达到了99.2%的准确率，展现了其优越性及与传统深度学习技术的可比性。最后，我们展示了模型在不同联邦客户端数量下的扩展性能。