Work

Task offloading and computing in vehicular edge networks: A joint of dynamic game and federated learning

Year: 2025

Type: article

Abstract: Federated Learning (FL) is well-suited for the Internet of Vehicles (IoV) due to its privacy-preserving and distributed nature. However, its application in Vehicular Edge Computing (VEC) faces key challenges: frequent task timeouts caused by high vehicle mobility, performance degradation from heterogeneous data, and the disjointed optimization of offloading and computation processes. To address these issues, this paper introduces a Edge Computing Service Provider (ECSP) to manage task allocation. We then decouple the VEC process into task offloading and computation. For the offloading process, we model it as a two-layer Stackelberg game and propose a novel Joint Dynamic Game and Federated Learning (JDGFL) algorithm. JDGFL enables dynamic strategy adaptation for the ECSP and vehicles, and integrates this with a three-tier "Vehicle-ECSP-RSU" FL architecture for the computation process. Extensive simulations demonstrate that our JDGFL strategy achieves significant performance improvements, reducing latency by 15 %, improving energy efficiency by 20 %, and increasing model accuracy by 5 % on average compared to static game-theoretic and standard FL baselines.

Source: Alexandria Engineering Journal

DOI: 10.1016/j.aej.2025.11.012

Authors: Dongmei Wang, Tong Wang, Xiaoxue Feng, Yalin Wu

Institutions: Hebei Vocational University of Technology and Engineering, Hong Kong University of Science and Technology, Peking University Shenzhen Hospital

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,900