CMBA-FL: Communication-mitigated and blockchain-assisted federated learning for traffic flow predictions^☆

Abstract

As an effective strategy to address urban traffic congestion, traffic flow prediction has gained attention from Federated-Learning (FL) researchers due FL's ability to preserving data privacy. However, existing methods face challenges: some are too simplistic to capture complex traffic patterns effectively, and others are overly complex, leading to excessive communication overhead between cloud and edge devices. Moreover, the problem of single point failure limits their robustness and reliability in real-world applications. To tackle these challenges, this paper proposes a new method, CMBA-FL, a Communication-Mitigated and Blockchain-Assisted Federated Learning model. First, CMBA-FL improves the client model's ability to capture temporal traffic patterns by employing the Encoder-Decoder framework for each edge device. Second, to reduce the communication overhead during federated learning, we introduce a verification method based on parameter update consistency, avoiding unnecessary parameter updates. Third, to mitigate the risk of a single point of failure, we integrate consensus mechanisms from blockchain technology. To validate the effectiveness of CMBA-FL, we assess its performance on two widely used traffic datasets. Our experimental results show that CMBA-FL reduces prediction error by 11.46%, significantly lowers communication overhead, and improves security.

Keywords

Blockchain / Communication mitigating / Federated learning / Traffic flow prediction

CRediT authorship contribution statement

Kaiyin Zhu: Methodology. Mingming Lu: Writing - review & editing, Supervision, Methodology, Funding acquisition, Conceptualization. Wenyong He: Writing - original draft, Software.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under Grant No. U20A20182. The authors thank AiMi Academic Services (www.aimieditor.com) for English language editing and review services.

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