基于数字孪生的车联网中的分层联邦迁移学习

中文摘要 AI 辅助

在近期基于数字孪生的车载自组织网络（DT-VANET）研究中，联邦学习（FL）展现出提供数据隐私的能力。然而，当面对车辆间的数据异质性和数据稀疏性时，联邦学习难以充分训练全局模型，导致对不同车辆类型的精准预测准确率欠佳。为应对这些挑战，本文结合联邦迁移学习（FTL）开展与车辆类型相关的车辆聚类，提出一种新型分层联邦迁移学习（HFTL）。我们构建了DT-VANET框架，设计了两种算法分别用于云服务器模型更新和集群内联邦迁移学习，以提升全局模型的准确率。此外，我们开发了基于数据质量评分的机制，防止全局模型受恶意车辆影响。最后，在真实数据集上开展了详细实验，考虑不同性能指标，验证了所提算法的有效性和效率。

英文摘要

In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy. However, Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles, which ensure suboptimal accuracy in making precise predictions for different vehicle types. To address these challenges, this paper combines Federated Transfer Learning (FTL) to conduct vehicle clustering related to types of vehicles and proposes a novel Hierarchical Federated Transfer Learning (HFTL). We construct a framework for DT-VANET, along with two algorithms designed for cloud server model updates and intra-cluster federated transfer learning, to improve the accuracy of the global model. In addition, we developed a data quality score-based mechanism to prevent the global model from being affected by malicious vehicles. Lastly, detailed experiments on real-world datasets are conducted, considering different performance metrics that verify the effectiveness and efficiency of our algorithm.

发表机构

• Georgia State University（佐治亚州立大学）
• Penn State Berks（宾州州立大学柏克斯分校）
• College of Information Sciences and Technology（信息科学与技术学院）

机构由 AI 辅助整理，请以论文原文为准。