Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles

LIU Miao; LIN Wanru; WANG Qin; GUI Guan

doi:10.11959/j.issn.1000-436x.2024170

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Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles

Comprehensive Reviews | 更新时间：2024-11-14

- Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles
- Journal on Communications Vol. 45, Issue 10, Pages: 207-224(2024)
- 作者机构：
  
  南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
- 基金信息：
  
  Scientific and Technological Innovation 2030—“New Generation Artificial Intelligence”the National Key Research and Development Program of China(2021ZD0113003)
- DOI：10.11959/j.issn.1000-436x.2024170
  CLC： TN92
- Received：05 June 2024，
  
  Revised：2024-09-03，
  
  Published：25 October 2024
- 稿件说明：
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刘淼,林婉茹,王琴等.车联网联邦学习的数据异质性问题及基于个性化的解决方法综述[J].通信学报,2024,45(10):207-224.

LIU Miao,LIN Wanru,WANG Qin,et al.Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles[J].Journal on Communications,2024,45(10):207-224.
刘淼,林婉茹,王琴等.车联网联邦学习的数据异质性问题及基于个性化的解决方法综述[J].通信学报,2024,45(10):207-224. DOI： 10.11959/j.issn.1000-436x.2024170.

LIU Miao,LIN Wanru,WANG Qin,et al.Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles[J].Journal on Communications,2024,45(10):207-224. DOI： 10.11959/j.issn.1000-436x.2024170.

摘要

在车联网（IoV）场景中，不同设备存在海量非独立同分布的数据，容易引发数据异质性问题，影响模型训练性能并威胁交通安全，对此聚焦于车联网联邦学习（FL）的数据异质性问题，通过对问题归因溯源提出了基于个性化的解决方法体系与研究新思路。首先，论述了联邦学习用于车联网的必要性，调研总结了车联网联邦学习中典型的数据异质性问题；其次，从感知、计算和传输3个环节对车联网联邦学习的数据异质性问题进行了分类和追踪；再次，引入个性化方法作为解决各类车联网联邦学习数据异质性问题的核心手段，并分析了现有个性化联邦学习的优点与不足；最后，讨论了个性化联邦学习在车联网场景中面临的研究挑战，并结合无线通信等相关技术展望了未来研究方向。

Abstract

In Internet of vehicles (IoV) scenario

there was a massive amount of non-independent and identically distributed data among devices

leading to data heterogeneity problems of federated learning (FL). This problem affected the performances of model training and might pose threats to traffic safety. Therefore

the focus lied on the data heterogeneity problem of FL in IoV

the personalized solution system and new research ideas were proposed through problem attribution. Firstly

the necessity of applying FL to IoV was discussed. Through an examination of current applications

identified the data heterogeneity problems of FL in IoV. Secondly

classified and traced the data heterogeneity problems of FL in IoV

from the perspective of perception

computation

and transmission respectively. Thirdly

personalized methods were introduced as the core approaches to address the data heterogeneity problems of FL in IoV

and analyzed the advantages and disadvantages of existing personalized federated learning (PFL). Finally

the challenges encountered by PFL in IoV were outlined

along with the future research prospection related to advanced technologies on wireless communications.

关键词

Keywords

references

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Related Author

Deng Bingguang

Peng Jiayin

Hu Ronglei

Bai Chenyang

Wei Zhanzhen

Han Yanyan

Duan Xiaoyi

Zhang Hao

Related Institution

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications

Department of Electronic and Communication Engineering, Beijing Electronic Science and Technology Institute

School of Information Engineering, Minzu University of China

School of Automation, Guangdong University of Technology

School of Cyberspace Security, Beijing Jiaotong University

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