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Evaluation Mechanism for Decentralised Collaborative Pattern Learning in Heterogeneous Vehicular Networks

Qiao, Cheng; Qiu, Jing; Tan, Zhiyuan; Min, Geyong; Zomaya, Albert Y.; Tian, Zhihong


Cheng Qiao

Jing Qiu

Geyong Min

Albert Y. Zomaya

Zhihong Tian


Collaborative machine learning, especially Feder-ated Learning (FL), is widely used to build high-quality Machine Learning (ML) models in the Internet of Vehicles (IoV). In this paper, we study the performance evaluation problem in an inherently heterogeneous IoV, where the final models across the network are not identical and are computed on different standards. Previous studies assume that local agents are receiving data from the same phenomenon, and a same final model is fitted to them. However, this "one model fits all" approach leads to a biased performance evaluation of individual agents. We propose a general approach to measure the performance of individual agents, where the common knowledge and correlation between different agents are explored. Experimental results indicate that our evaluation scheme is efficient in these settings.

Journal Article Type Article
Acceptance Date Jun 23, 2022
Online Publication Date Jul 12, 2022
Publication Date 2023-11
Deposit Date Jul 5, 2022
Publicly Available Date Jul 12, 2022
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 24
Issue 11
Pages 13123 - 13132
Keywords Internet of Vehicles; Distributed algorithm; Clustering; Similarity measurement
Public URL


Evaluation Mechanism For Decentralised Collaborative Pattern Learning In Heterogeneous Vehicular Networks (accepted version) (1.1 Mb)

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