Hierarchical Decentralized Federated Learning Framework with Adaptive Clustering: Bloom-Filter-Based Companions Choice for Learning Non-IID Data in IoV

doi:10.3390/electronics12183811

	Hierarchical Decentralized Federated Learning Framework with Adaptive Clustering: Bloom-Filter-Based Companions Choice for Learning Non-IID Data in IoV
	Liu, Siyuan 1; Liu, Zhiqiang 1; Xu, Zhiwei 2,3; Liu, Wenjing 4; Tian, Jie 5
	2023-09-01
发表期刊	ELECTRONICS
卷号	12 期号:18 页码:18
摘要	The accelerating progress of the Internet of Vehicles (IoV) has put forward a higher demand for distributed model training and data sharing in vehicular networks. Traditional centralized approaches are no longer applicable in the face of drivers' concerns about data privacy, while Decentralized Federated Learning (DFL) provides new possibilities to address this issue. However, DFL still faces challenges regarding the non-IID data of passing vehicles. To tackle this challenge, a novel DFL framework, Hierarchical Decentralized Federated Learning (H-DFL), is proposed to achieve qualified distributed training among vehicles by considering data complementarity. We include vehicles, base stations, and data center servers in this framework. Firstly, a novel vehicle-clustering paradigm is designed to group passing vehicles based on the Bloom-filter-based compact representation of data complementarity. In this way, vehicles train their models based on local data, exchange model parameters in each group, and achieve a qualified local model without the interference of imbalanced data. On a higher level, a local model trained by each group is submitted to the data center to obtain a model covering global features. Base stations maintain the local models of different groups and judge whether the local models need to be updated according to the global model. The experimental results based on real-world data demonstrate that H-DFL dose not only reduces communication latency with different participants but also addresses the challenges of non-IID data in vehicles.
关键词	Internet of Vehicles hierarchical decentralized federated learning in-network compact representation vehicle clustering
DOI	10.3390/electronics12183811
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61962044] ; National Natural Science Foundation of China[61962045] ; Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region[NJYT23104] ; Inner Mongolia Science and Technology Plan Project[2021GG0250] ; Natural Science Foundation of Inner Mongolia Autonomous Region[2021MS06029] ; Basic scientific research business fund project of universities directly under the autonomous region[JY20220324] ; Inner Mongolia Autonomous Region Higher Education Scientific Research Project[NJZZ22428]
WOS研究方向	Computer Science ; Engineering ; Physics
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Physics, Applied
WOS记录号	WOS:001078758000001
出版者	MDPI
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21132
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Zhiqiang
作者单位	1.Inner Mongolia Univ Technol, Coll Informat Engn, Hohhot 010051, Peoples R China 2.Haihe Lab Informat Technol Applicat Innovat, Tianjin 300350, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China 4.Inner Mongolia Univ Technol, Coll Data Sci & Applicat, Hohhot 010051, Peoples R China 5.New Jersey Inst Technol, Dept Comp Sci, Newark, NJ 07102 USA
推荐引用方式 GB/T 7714	Liu, Siyuan,Liu, Zhiqiang,Xu, Zhiwei,et al. Hierarchical Decentralized Federated Learning Framework with Adaptive Clustering: Bloom-Filter-Based Companions Choice for Learning Non-IID Data in IoV[J]. ELECTRONICS,2023,12(18):18.
APA	Liu, Siyuan,Liu, Zhiqiang,Xu, Zhiwei,Liu, Wenjing,&Tian, Jie.(2023).Hierarchical Decentralized Federated Learning Framework with Adaptive Clustering: Bloom-Filter-Based Companions Choice for Learning Non-IID Data in IoV.ELECTRONICS,12(18),18.
MLA	Liu, Siyuan,et al."Hierarchical Decentralized Federated Learning Framework with Adaptive Clustering: Bloom-Filter-Based Companions Choice for Learning Non-IID Data in IoV".ELECTRONICS 12.18(2023):18.