基于联邦学习的车联网多维资源动态分配算法  

Multi-dimensional Resource Dynamic Allocation Algorithm for Internet of Vehicles Based on Federated Learning

作　　者：吴赟寒 白光伟 沈航 WU Yun-han;BAI Guang-wei;SHEN Hang(College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China)

机构地区：[1]南京工业大学计算机科学与技术学院,南京211816

出　　处：《计算机科学》2022年第12期59-65,共7页Computer Science

基　　金：国家自然科学基金(61502230);江苏省自然科学基金(BK20201357)。

摘　　要：考虑到车联网系统中多维资源消耗会随时间波动的特性和用户对高效计算服务以及数据隐私安全的需求,提出了一种基于联邦学习的车联网多维资源分配方法。一方面,综合考虑计算、缓存和带宽资源分配,保证计算任务的完成率,避免多维资源的冗余分配,基于该目标设计了一种深度学习算法,通过边缘服务器收集的数据预测各项资源的消耗量,以此为依据分配多维资源;另一方面,考虑到用户的数据隐私安全需求造成的数据孤岛问题,采用联邦学习架构以获得泛化性较好的神经网络模型。该算法能随时间调整多维资源的分配量,满足随时间变动的资源需求,保证车联网系统中计算任务的高效完成。实验结果表明该算法具有收敛速度快、模型泛化性好等特点,能以较少的通信轮数完成联邦学习的聚合。In consideration of the characteristics of multi-dimensional resource consumption fluctuating with time in the Internet of Vehicles system and users’demands for efficient computing services and data privacy and security,this paper proposes a me-thod of multi-dimensional resource allocation for Internet of Vehicles based on federated learning.On the one hand,the allocation of computing,cache and bandwidth resources is considered comprehensively to ensure the completion rate of computing tasks and avoid the redundant allocation of multidimensional resources.For this purpose,a deep learning algorithm is designed to predict the consumption of various resources through the data collected by edge servers.On the other hand,considering the data island problem caused by users’data privacy and security requirements,federated learning architecture is adopted to obtain a neural network model with better generalization.The proposed algorithm can not only adjust the allocation of multi-dimensional resources over time,but also meet the resource requirements that change over time,and ensure the efficient completion of computing tasks in the Internet of Vehicles system.Experimental results show that the algorithm has the characteristics of fast convergence and good model generalization,and can complete the aggregation of federated learning with fewer communication rounds.

关 键 词：车辆网络 联邦学习 多维资源分配 计算迁移 机器学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]