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基于编-解码器结构的无人机群多任务联邦学习

周敬轩 包卫东 王吉 张大宇

周敬轩, 包卫东, 王吉, 张大宇. 基于编-解码器结构的无人机群多任务联邦学习[J]. 西南交通大学学报, 2024, 59(4): 933-941. doi: 10.3969/j.issn.0258-2724.20230539
引用本文: 周敬轩, 包卫东, 王吉, 张大宇. 基于编-解码器结构的无人机群多任务联邦学习[J]. 西南交通大学学报, 2024, 59(4): 933-941. doi: 10.3969/j.issn.0258-2724.20230539
ZHOU Jingxuan, BAO Weidong, WANG Ji, ZHANG Dayu. Multi-Task Federated Learning for Unmanned Aerial Vehicle Swarms Based on Encoder-Decoder Architecture[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 933-941. doi: 10.3969/j.issn.0258-2724.20230539
Citation: ZHOU Jingxuan, BAO Weidong, WANG Ji, ZHANG Dayu. Multi-Task Federated Learning for Unmanned Aerial Vehicle Swarms Based on Encoder-Decoder Architecture[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 933-941. doi: 10.3969/j.issn.0258-2724.20230539

基于编-解码器结构的无人机群多任务联邦学习

doi: 10.3969/j.issn.0258-2724.20230539
基金项目: 国家自然科学基金(62002369);湖南省研究生科研创新项目(XJCX2023013)
详细信息
    作者简介:

    周敬轩(1994—),男,博士研究生,研究方向为多任务学习,E-mail:zhoujingxuan@nudt.edu.cn

    通讯作者:

    包卫东(1971—),男,教授,博士,研究方向为指挥信息系统,E-mail:wdbao@nudt.edu.cn

  • 中图分类号: TP183

Multi-Task Federated Learning for Unmanned Aerial Vehicle Swarms Based on Encoder-Decoder Architecture

  • 摘要:

    针对传统联邦学习在无人机群应用中的局限性——要求所有参与者执行相同任务并拥有相同的模型结构,本文探索一种适用于无人机群的多任务联邦学习方法,设计一种新的编-解码器架构,以加强执行不同任务的无人机之间的知识共享. 首先,为执行相同任务的无人机建立直接的知识分享机制,通过直接聚合方式实现同任务知识的有效融合;其次,对于执行不同任务的无人机,从所有无人机的编-解码器架构中提取编码器部分,构建一个全局编码器;最后,在训练环节,将本地编码器和全局编码器的信息整合到损失函数中,并通过迭代更新使本地解码器逐步逼近全局解码器,从而实现跨任务间的知识高效共享. 实验结果表明:相较于传统方法,所提出的方法使无人机群在3个单任务上的性能分别提升1.79%、0.37%和2.78%,仅在1个任务上性能略微下降0.38%,但整体性能仍提升2.38%.

     

  • 图 1  无人机群M-Fed方法与其他典型方法比较

    Figure 1.  Comparison of M-Fed method for UAV swarms with other typical methods

    图 2  M-Fed方法总体框架

    Figure 2.  Overall framework for M-Fed method

    表  1  PASCAL-Context数据集实验结果

    Table  1.   Experimental results on PASCAL-Context dataset

    方法 编码器 语义分割 人体部位分割 显著性估计 语义边界检测 增量/%
    局部训练 ResNet-18 38.36 49.03 55.74 60.90 0
    经典联邦学习 ResNet-18 49.71 52.66 59.09 61.20 10.87
    M-Fed ResNet-18 50.60 52.46 59.31 62.90 13.25
    下载: 导出CSV

    表  2  PASCAL-Context数据集的量化结果

    Table  2.   Quantitative results on PASCAL-Context dataset

    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-15
  • 修回日期:  2024-04-10
  • 网络出版日期:  2024-05-29
  • 刊出日期:  2024-04-17

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