mlsanityData to 14 Oct 2026

Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application

arXiv 2301.04829 ↗First posted 12 Jan 2023cs.LG cs.CRcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 21 of its 48 references that are in the map. First posted on arXiv 12 Jan 2023.

Builds on

21 of 48 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
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  10. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
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  12. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
9 more
  1. 2020Threats to Federated Learning: A Surveycited by 178
  2. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  3. 2019Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Modelscited by 118
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Cited by year

Citing papers per year: 2023: 8; 2024: 3.

11 papers.

  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  3. 2023Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognitioncited by 19
  4. 2023A Survey of Federated Learning for Connected and Automated Vehiclescited by 17
  5. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 11
  6. 2023M2DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformercited by 5
  7. 2023FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communicationcited by 4
  8. 2023Digital Ethics in Federated Learningcited by 2
  9. 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Unitscited by 2
  10. 2024Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guaranteescited by 1
  11. 2023Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognitioncited by 0

Similar, not cited

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  1. 2026FedTransKD-IDS: Robust Federated Transfer Learning with Knowledge Distillation for Intrusion Detection in IoT
  2. 2023REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments
  3. 2025Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
  4. 2025FLAD: Federated Learning for LLM-based Autonomous Driving in Vehicle-Edge-Cloud Networks
  5. 2021Deep Federated Learning for Autonomous Driving
  6. 2021Privacy Assessment of Federated Learning using Private Personalized Layers
  7. 2021Density-Aware Federated Imitation Learning for Connected and Automated Vehicles with Unsignalized Intersection ↗
  8. 2024Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration