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 applications (DMAs) 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, and personalized framework for DMA.

翻译：联邦学习（FL）凭借其通过共享本地训练客户端的模型参数实现协作学习、提升学习效率的能力，在物联网（IoT）中表现出色。尽管FL已成功应用于包括车联网（IoV）驾驶员监测应用（DMA）在内的多个领域，但其仍面临数据与系统异构性、大规模并行通信资源、恶意攻击及数据投毒等开放性问题。本文提出一种联邦迁移有序个性化学习（FedTOP）框架以解决上述问题，并在两个真实数据集上（分别考虑系统异构性存在与否的情况）进行测试。通过消融实验比较了迁移、有序和个性化三种扩展性能，在两个数据集的测试客户端上分别达到92.32%和95.96%的准确率。与基线相比，准确率提升462%，通信资源消耗降低37.46%。实验结果表明，所提出的FedTOP可作为面向DMA的高精度、轻量化、隐私保护、网络安全导向的个性化框架。