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双因子更新的车联网双层异步联邦学习研究

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针对车联网(IoV)中节点资源异构、拓扑结构动态变化等特点,该文建立了一个双因子更新的双层异步联邦学习(TTAFL)框架.考虑到模型版本差和车辆参与联邦学习(FL)次数对局部模型更新的影响,提出基于陈旧因子和贡献因子的模型更新方案.同时,为了避免训练过程中,车辆移动带来路侧单元切换的问题,给出考虑驻留时间的节点选择方案.最后,为了减少精度损失与系统能耗,利用强化学习方法优化联邦学习的本地迭代次数与路侧单元局部模型更新次数.仿真结果表明,所提算法有效提高了联邦学习的训练效率和训练精度,降低了系统能耗.
A Study of Two-layer Asynchronous Federated Learning with Two-factor Updating for Vehicular Networking
In response to the characteristics of heterogeneous node resources and dynamic changes in the network topology in the Internet of Vehicles(IoV),a Two-layer Asynchronous Federated Learning with Two-factor updating(TTAFL)framework is established in this paper.Considering the impact of model version differences and the number of times that vehicles participate in Federated Learning(FL)on server model updates,a model update scheme based on staleness factor and contribution factor is proposed.Furthermore,to avoid the problem of roadside unit switching caused by vehicle mobility during the training process,a node selection scheme considering the residence time is given.Finally,in order to reduce the accuracy loss and system energy consumption,a reinforcement learning method is used to optimize the number of local iterations of FL and the number of local model updates of roadside units.Simulation results show that the proposed algorithm effectively improves the training efficiency and training accuracy of federated learning and reduces the system energy consumption.

Internet of Vehicles(IoV)Federated Learning(FL)Asynchronous trainingDeep reinforcement learning

王力立、吴守林、杨妮、黄成

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南京理工大学自动化学院 南京 210094

车联网 联邦学习 异步训练 深度强化学习

2024

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子:1.302
ISSN:1009-5896
年,卷(期):2024.46(7)
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