  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.
