Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles
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....
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-10-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024170/ |
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| _version_ | 1841537114744815616 |
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| author | LIU Miao LIN Wanru WANG Qin GUI Guan |
| author_facet | LIU Miao LIN Wanru WANG Qin GUI Guan |
| author_sort | LIU Miao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3fa77a7f9a3a4c1786d4fa5bea004200 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-3fa77a7f9a3a4c1786d4fa5bea0042002025-01-14T08:46:01ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-10-014520722477077529Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehiclesLIU MiaoLIN WanruWANG QinGUI GuanIn 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024170/Internet of vehiclesfederated learningpersonalized solutiondata heterogeneity |
| spellingShingle | LIU Miao LIN Wanru WANG Qin GUI Guan Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles Tongxin xuebao Internet of vehicles federated learning personalized solution data heterogeneity |
| title | Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles |
| title_full | Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles |
| title_fullStr | Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles |
| title_full_unstemmed | Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles |
| title_short | Survey on data heterogeneity problems and personalization based solutions of federated learning in Internet of vehicles |
| title_sort | survey on data heterogeneity problems and personalization based solutions of federated learning in internet of vehicles |
| topic | Internet of vehicles federated learning personalized solution data heterogeneity |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024170/ |
| work_keys_str_mv | AT liumiao surveyondataheterogeneityproblemsandpersonalizationbasedsolutionsoffederatedlearningininternetofvehicles AT linwanru surveyondataheterogeneityproblemsandpersonalizationbasedsolutionsoffederatedlearningininternetofvehicles AT wangqin surveyondataheterogeneityproblemsandpersonalizationbasedsolutionsoffederatedlearningininternetofvehicles AT guiguan surveyondataheterogeneityproblemsandpersonalizationbasedsolutionsoffederatedlearningininternetofvehicles |