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作 者:庄琲 韩志博 聂锦标 李子怡 林尚静[1] ZHUANG Bei;HAN Zhibo;NIE Jinbiao;LI Ziyi;LIN Shangjing(Beijing Key Laboratory of Work Safety Intelligent Monitoring,Beijing University of Posts and Telecommunications,Beijing 100876,China)
机构地区:[1]北京邮电大学安全生产智能监控北京市重点实验室,北京100876
出 处:《无线电通信技术》2024年第1期203-212,共10页Radio Communications Technology
基 金:国家自然科学基金中英爱丁堡皇家国际合作交流基金(61701034)。
摘 要:联邦学习框架正逐渐被广泛应用于车联网领域,针对车辆的移动性特点以及大量车辆联邦学习时同时接入参数服务器交互参数易产生的通信拥塞的问题,提出了一种基于模糊逻辑的车辆选择和基于演化博弈的参数服务器选择算法。通过设计模糊逻辑算法,考虑车辆的移动性、设备条件以及数据量因素来选择通信连接较稳定、计算能力较强和数据量多的车辆参与联邦学习。采用演化博弈进一步刻画选出的车辆自主决策选择参数服务器的过程,平衡联邦学习模型准确度以及产生的通信和计算成本,从而避免通信拥塞并最大化车辆个体利益和整体利益。仿真验证了大量车辆场景下所提出算法的性能,实现模型训练的低成本、高精度。The federated learning framework is gradually being widely applied in the internet of vehicles.In response to the mobility characteristics of vehicles,as well as the communication congestion problem caused by the simultaneous access of parameter server i nteraction parameters during federated learning of a large number of vehicles,a fuzzy logic based vehicle selection algorithm and a p arameter server selection algorithm based on evolutionary game are proposed.By designing a fuzzy logic algorithm and considering the mobility of vehicles,equipment conditions,and data size factors,vehicles are selected with stable communication connections,high computing power,and large data size to participate in federated learning.Evolutionary game is used to further characterize the process of selecting parameter servers for autonomous vehicle decision-making.The accuracy of federated learning models and the resulting communication and computational costs is balanced.Thereby communication congestion is avoided and individual and overall profit is maximized.Finally,simulation result verifies the performance of the proposed algorithm in a large number of vehicle scenarios,a chieving low-cost and high-precision model training.
关 键 词:车联网 联邦学习 演化博弈 模糊逻辑 复制者动态
分 类 号:TN92[电子电信—通信与信息系统]
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