Title: FLVAMatch: an economic matching game theory-based vehicle selection in federated learning for the next-generation of internet of vehicles

Authors: L. Jai Vinita; V. Vetriselvi

Addresses: Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India ' Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India

Abstract: The future generation of the internet of vehicles (IoV) in intelligent transportation systems (ITS) leverages advanced communication and intelligent data analysis. Federated learning (FL) enhances IoV privacy, but conventional random-based vehicle selection for local training is inefficient due to varying client resources and data quality. Existing methods prioritise server preferences, overlooking FL-vehicle incentives. To address this, we formulate a profit maximisation problem to enhance FL-vehicle revenues and propose FLVAMatch, an economic framework based on the hospital-resident (H-R) matching problem. In a three-layer software-defined vehicular fog (SDVF) computing setup, FLVAMatch considers both FL-vehicle and aggregator preferences. Simulation results demonstrate its effectiveness in maximising FL-vehicle revenues and reducing network latency, outperforming state-of-the-art FL approaches, with the global model achieving 88% accuracy.

Keywords: internet of vehicles; IoV; fog computing; federated learning; vehicle selection; matching game theory.

DOI: 10.1504/IJSNET.2025.146777

International Journal of Sensor Networks, 2025 Vol.48 No.2, pp.65 - 82

Received: 08 Jan 2024
Accepted: 24 Jun 2024
Published online: 17 Jun 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article