ITSC 2025 Paper Abstract

Paper FR-LM-T40.6

Zeng, Lingqiu (Chongqing University), Huang, Yong (Chongqing University), Xie, Fukun (Chongqing University), Han, Qingwen (Chongqing University), Ye, Lei (Chongqing University)

FedMATD3: A Federated Reinforcement Learning Approach for Global Optimization in Multi-Agent Vehicular Task Offloading

Scheduled for presentation during the Regular Session "S40a-Cooperative and Connected Autonomous Systems" (FR-LM-T40), Friday, November 21, 2025, 12:10−12:30, Cooleangata 4

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Multi-vehicle Coordination for Autonomous Fleets in Urban Environments, Cloud and Edge Computing Integration in ITS for Real-time Traffic Data Processing, AI, Machine Learning and Predictive Analytics for Traffic Incident Detection and Management

Abstract

With the rapid development of intelligent transportation systems, task offloading technologies have been widely adopted to shift computational workloads from resource-constrained vehicles to resource-rich edge and cloud servers, thereby improving the service quality of Internet of Vehicles (IoV) systems. However, the growing complexity of vehicular applications and the heterogeneity of service demands have led to critical challenges, such as dynamic resource imbalance, cross-domain scheduling difficulties, and inefficiencies in global optimization. These challenges require more intelligent and coordinated task offloading mechanisms capable of handling resource diversity and system-wide collaboration. To address these issues, a novel task-offloading model based on a federated reinforcement learning framework is proposed. Specifically, a Device-Edge-Cloud collaborative offloading model is constructed, and the Federated Learning Optimized Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (FedMATD3) algorithm is developed to achieve global optimization and efficient cross-domain resource allocation. Extensive simulation experiments validate the effectiveness of the proposed approach. Compared with four classical baseline algorithms, FedMATD3 demonstrates superior performance in terms of task processing delay and task completion rate.