异质车联网数据的群联邦迁移学习共享方法研究  

Research on shared method for swarm federated transfer learning of heterogeneous internet of vehicles

作　　者：康海燕[1] 柯慧敏 邱晓英 KANG Haiyan;KE Huimin;QIU Xiaoying(Department of Information Security,Beijing Information Science and Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学信息安全系,北京100192

出　　处：《重庆理工大学学报(自然科学)》2025年第2期1-10,共10页Journal of Chongqing University of Technology:Natural Science

基　　金：国家社会科学基金项目(21BTQ079);未来区块链与隐私计算高精尖创新中心基金项目(GJJ-23-001);北京市教育委员会科技计划项目(KM202011232022)。

摘　　要：为了解决联邦学习在车联网中终端设备数据的异质性导致模型训练准确率不稳定和性能下降,以及车辆分布广泛,通信和计算资源有限的问题,提出一种数据类型和数据规模并行优化的群联邦迁移学习数据共享方法(swarm federated transfer learning,SFTL)。提出基于高斯混合模型的共识设备组划分机制,通过对数据分布建模构建共识设备组,实现对异质性数据的有效管理和分析;面向划分的共识设备组,设计蜂群学习训练机制,加强相似设备组之间的协同学习过程;提出组间迁移学习机制,通过模型预训练法增量迁移不同共识设备组信息最小化模型差异,提高联邦模型聚合准确率。在公共数据集上的实验表明:与基线方法相比,SFTL模型训练准确率平均提高7%,通信时间平均降低10%。The Internet of Vehicles(IoV)have become an integral part of intelligent transportation systems,offering significant advantages in traffic management,safety,and efficiency.However,the rapid development of IoV also poses big challenges,particularly in terms of data heterogeneity and resource constraints.The diverse types of vehicles,sensors,and driving environments generate highly heterogeneous data,which traditional federated learning methods struggle to handle effectively.These methods often suffer unstable training accuracy and performance degradation due to varied data distributions and limited communication and computational resources available in vehicular networks.To address these challenges,we propose a novel Swarm Federated Transfer Learning(SFTL)method,which optimizes the training process for different data types and scales while enhancing communication efficiency.In our study,three innovations are made.The first in the SFTL method is the consensus device group division mechanism,which is based on the Gaussian Mixture Model(GMM).The GMM is a probabilistic model that assumes all the data points are generated from a mixture of several Gaussian distributions with unknown parameters.By modeling the data distribution,the GMM dynamically identifies potential cluster structures within the data,enabling the segmentation of devices into consensus groups based on their data characteristics.This mechanism effectively manages and analyzes heterogeneous data by grouping devices with similar data distributions together.The GMM-based clustering ensures each group contributes meaningfully to the overall learning process,thereby addressing the inherent heterogeneity of data in the IoV.The second is the Swarm Learning(SL)training mechanism.It leverages blockchain technology to form a decentralized swarm network at the roadside unit level.Each consensus group trains its local model independently within this swarm network.The decentralized nature of the swarm network ensures robustness and security,as it eliminates the sing

关 键 词：蜂群学习 联邦学习 车联网 高斯混合模型 迁移学习 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]