计算机与现代化Issue(7)：1-11,11.DOI:10.3969/j.issn.1006-2475.2026.07.001
基于多智能体深度强化学习的车联网任务安全卸载
Secure Task Offloading for IoV Based on Multi-agent Deep Reinforcement Learning
摘要
Abstract
With the development of intelligent transportation,the integration of mobile edge computing has alleviated the re-source limitations of vehicles in the Internet of Vehicles(IoV).However,the lack of efficient task offloading strategies still af-fects the Quality of Experience(QoE)of users.To address the security issues during task offloading,this paper proposes a task security offloading strategy based on Multi-Agent Deep Reinforcement Learning(MADRL).Firstly,a task model,acommunica-tion model,and a computation model are established based on the dynamic environment of vehicles.The task offloading problem is then transformed into a Markov Decision Process(MDP)according to the system model.Secondly,the Multi-Agent Deep De-terministic Policy Gradient(MADDPG)algorithm is utilized to handle the convergence issues of a single agent in a dynamic envi-ronment,ensuring the efficient offloading of tasks.Finally,differential privacy and weighted federated learning are incorporated into the algorithm training process to mitigate the impact of heterogeneous edge node resources and Non-IID data on the training results,while also preventing attackers from inferring the model through parameter reverse engineering,thus protecting user pri-vacy.Simulation results show that the proposed algorithm can effectively improve system execution efficiency while ensuring user privacy during the training process.关键词
车联网/边缘计算/深度强化学习/任务卸载/联邦学习/差分隐私Key words
Internet of Vehicles/edge computing/deep reinforcement learning/task offloading/federal learning/differential privacy分类
信息技术与安全科学引用本文复制引用
孙静文,高赫..基于多智能体深度强化学习的车联网任务安全卸载[J].计算机与现代化,2026,(7):1-11,11.基金项目
国家自然科学基金资助项目(62207020) （62207020）
陕西省科技厅项目(2023QCY-LL-34,2023QYPY-14) （2023QCY-LL-34,2023QYPY-14）
西安市科技局项目(2023JH-QCYCK-0030) （2023JH-QCYCK-0030）
秦都区科技局项目(2024ZH001) （2024ZH001）
