Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles

Saputra, Yuris Mulya and Hoang, Dinh Thai and Nguyen, Diep N. and Tran, Le-Nam and Gong, Shimin and Dutkiewicz, Eryk (2023) Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles. IEEE Transactions on Mobile Computing, 22 (4). 2100 – 2115. ISSN 15361233

Full text not available from this repository. (Request a copy)

Abstract

Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure. The collected data and learned knowledge can help the vehicular service provider (VSP) improve the global model accuracy, e.g., for road safety as well as better profits for both VSP and participating SVs. Nonetheless, there exist major challenges when implementing the FL in IoV networks, such as dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information history at each learning round. Then, each selected SV can collect on-road information and propose a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the VSP's limited payment budget and asymmetric information between the VSP and SVs. Through experimental results using real-world on-road datasets, we show that our framework can converge 57 faster (even with only 10 of active SVs in the network) and obtain much higher social welfare of the network (up to 27.2 times) compared with those of other baseline FL methods. © 2002-2012 IEEE.

Item Type: Article
Additional Information: Cited by: 25; All Open Access, Green Open Access
Uncontrolled Keywords: Accident prevention; Budget control; Cloud computing; Computation theory; Motor transportation; Profitability; Roads and streets; Vehicles; Contract Theory; Convergence; Federated learning; Internet-of-vehicle; Mobile-computing; Optimisations; Profit optimization; Quality-of-information; Road safety; Vehicle's dynamics; Vehicular networks; Mobile computing
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Vocational School
Depositing User: Sri JUNANDI
Date Deposited: 04 Nov 2024 01:18
Last Modified: 04 Nov 2024 01:18
URI: https://ir.lib.ugm.ac.id/id/eprint/10515

Actions (login required)

View Item
View Item