Federated learning (FL) shines through in the internet of things (IoT) with its ability to realize collaborative learning and improve learning efficiency by sharing client model parameters trained on local data. Although FL has been successfully applied to various domains, including driver monitoring application (DMA) on the internet of vehicles (IoV), its usages still face some open issues, such as data and system heterogeneity, large-scale parallelism communication resources, malicious attacks, and data poisoning. This paper proposes a federated transfer-ordered-personalized learning (FedTOP) framework to address the above problems and test on two real-world datasets with and without system heterogeneity. The performance of the three extensions, transfer, ordered, and personalized, is compared by an ablation study and achieves 92.32% and 95.96% accuracy on the test clients of two datasets, respectively. Compared to the baseline, there is a 462% improvement in accuracy and a 37.46% reduction in communication resource consumption. The results demonstrate that the proposed FedTOP can be used as a highly accurate, streamlined, privacy-preserving, cybersecurity-oriented, personalized framework for DMA.

翻译：联邦学习(FL)在互联网上闪耀着事物(IoT),它有能力通过分享当地数据培训的客户模型参数实现协作学习和提高学习效率。虽然FL成功地应用于各个领域,包括车辆互联网(IoV)上的驱动器监测应用程序(DMA),但其使用仍面临一些尚未解决的问题,如数据和系统差异性、大规模平行通信资源、恶意袭击和数据中毒。本文件提议建立一个联合传输命令个人化学习(FedTOP)框架,以解决上述问题,并测试两个真实世界数据集,不论是否系统异质性。三个扩展、转移、订购和个性化的绩效,通过消化研究加以比较,在两个数据集的测试客户上分别达到92.32%和95.96%的精度。与基线相比,准确性提高了462%,通信资源消耗率减少了37.46%。结果显示,拟议的FDTOP可用作高度准确、简化、隐私、面向安全管理DMA的个人框架。