Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application
| Autor: | Liangqi Yuan, Lu Su, Ziran Wang |
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| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Cryptography and Security Computer Networks and Communications Hardware and Architecture Signal Processing Cryptography and Security (cs.CR) Computer Science Applications Information Systems Machine Learning (cs.LG) |
| DOI: | 10.48550/arxiv.2301.04829 |
| Popis: | 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. Comment: IEEE Internet of Things Journal |
| Databáze: | OpenAIRE |
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