Internet of Vehicles: Architectures, Protocols and Communication Technologies

Aramice, Gregor Alexander; Miry, Abbas H.; Salman, Tariq M.

doi:10.1109/icmi55296.2022.9873699

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Internet of vehicles (IoV) is a new paradigm in the future Internet of things, where vehicles sense external information through on-board equipment on the road [1][2][3][4]. Vehicles can obtain huge amounts of data (e.g., video data), and the circulation of data among vehicles is one of the most effective means to improve the efciency of data utilization and fully explore the value of vehicular data [5][6][7][8]. However, due to concerns about personal privacy data leakage, vehicle owners are unwilling to upload data to the data center, which hinders the fow of data and afects the development of the IoV industry [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Towards Efficient Federated Learning Using Agile Aggregation in Internet of Vehicles

He,

Hu,

Wang

et al. 2023

Security and Communication Networks

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Federated learning is an enabling technology for the services in Internet of vehicles because it can effectively alleviate privacy issues in data circulation and diversified intelligent applications. However, existing federated learning methods still confront the problem of low computational efficiency when applied to the scenario of high-dynamic vehicles. In order to address this problem, we present a multistep federated learning architecture including task release, vehicle model training, identity authentication, quality assessment, and weighted aggregation processes, where a novel agile aggregation model is established. Based on the agile aggregation model, an efficient federated learning (EFL) algorithm is proposed, which is specifically applied to the scenario of high-dynamic vehicles during the training model. We theoretically analyze its advantages over the existing traditional federated learning algorithms in terms of efficiency, training accuracy, and security. Simulations are conducted using the MNIST dataset to evaluate the performance of the proposed EFL algorithm. The results show that EFL improves the efficiency of the federated training process with effective accuracy and privacy preservation.

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