Driver fatigue is a critical safety concern in advanced driver assistance systems. Driver monitoring models trained off-site on static datasets adapt poorly to real-world conditions, while standard federated learning imposes high communication overhead, assumes homogeneous architectures, and struggles with personalized driver data. We present FedADAS, a federated distillation framework enabling collaborative on-device learning across heterogeneous vehicular networks. FedADAS enables full model heterogeneity by exchanging only soft logits on a shared public dataset, allowing each vehicle to run a customized model tailored to its computational constraints. Additionally, we introduce a yawn recognition pipeline supporting training and inference on edge devices that provides two robust architectures: Performance-Efficient (99.7 MB) achieving 98.3% F1-score with 1.99ms inference time on a Jetson NANO, and a Memory-Efficient (0.6 MB) that trains an epoch in 6.12 minutes on a Jetson AGX Orin. In experiments with up to 115 edge clients, FedADAS significantly outperforms traditional federated learning approaches at higher client participation, achieving up to 9974x reduction in communication cost while maintaining a superior tradeoff between personalization and generalization under extreme data heterogeneity, demonstrating its suitability for real-world deployment. Code is available at https://opensource.silicon-austria.com/mujtabaa/fedadas

翻译：驾驶员疲劳是高级驾驶辅助系统中的关键安全问题。在静态数据集上离线训练的驾驶员监测模型难以适应真实场景，而标准联邦学习存在通信开销大、架构同质化假设及个性化数据适配困难等局限。我们提出FedADAS——一种支持异构车载网络协同在线学习的联邦蒸馏框架。通过仅在共享公共数据集上交换软logits，FedADAS实现了完全模型异构性，使每辆车可运行适配其计算约束的定制化模型。此外，我们引入了一套支持边缘设备训练与推理的哈欠识别流水线，提供两种鲁棒架构：性能高效模型（99.7 MB）在Jetson NANO上达到98.3%的F1分数、推理时间1.99ms；内存高效模型（0.6 MB）在Jetson AGX Orin上单轮训练仅需6.12分钟。在包含115个边缘客户端的实验中，FedADAS在高参与率场景下显著超越传统联邦学习方法，通信成本降低高达9974倍，并在极端数据异构条件下保持了个性化与泛化性能的优越平衡，证实了其实际部署的可行性。开源代码见https://opensource.silicon-austria.com/mujtabaa/fedadas