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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. 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. 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. 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. 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

    图 3  无人机群M-Fed算法

    Figure 3.  M-Fed algorithm for UAV swarms

    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

  • [1] 王志红,程尚. 无人驾驶场景多任务感知算法研究[J]. 武汉理工大学学报,2023,45(8): 140-146. doi: 10.3963/j.issn.1671-4431.2023.08.021

    WANG Zhihong, CHENG Shang. Research on multitask perception algorithms for unmanned driving scenarios[J]. Journal of Wuhan University of Technology, 2023, 45(8): 140-146. doi: 10.3963/j.issn.1671-4431.2023.08.021
    [2] 杜家豪,秦娜,贾鑫明,等. 基于联邦学习的多线路高速列车转向架故障诊断[J]. 西南交通大学学报,2024,59(1): 185-192.

    DU Jiahao, QIN Na, JIA Xinming, et al. Fault diagnosis of multiple railway high speed train bogies based on federated learning[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 185-192.
    [3] 姚献财,郑建超,郑鑫,等. 面向联邦学习的无人机轨迹与资源联合优化[J/OL]. 计算机工程与应用,1-12 [2024-04-28]. http://kns.cnki.net/kcms/detail/11.2127.TP.20230 509.1840.007.html.
    [4] SHEN Y, QU Y B, DONG C, et al. Joint training and resource allocation optimization for federated learning in UAV swarm[J]. IEEE Internet of Things Journal, 2023, 10(3): 2272-2284. doi: 10.1109/JIOT.2022.3152829
    [5] MO X P, XU J. Energy-efficient federated edge learning with joint communication and computation design[J]. Journal of Communications and Information Networks, 2021, 6(2): 110-124. doi: 10.23919/JCIN.2021.9475121
    [6] PHAM Q V, LE M, HUYNH-THE T, et al. Energy-efficient federated learning over UAV-enabled wireless powered communications[J]. IEEE Transactions on Vehicular Technology, 2022, 71(5): 4977-4990. doi: 10.1109/TVT.2022.3150004
    [7] HINTON G, VINYALS O, DEAN J. Distilling the knowledge in a neural network[J]. Computer Science, 2015, 14(7): 38-39.
    [8] XU D, OUYANG W L, WANG X G, et al. PAD-net: multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 675-684.
    [9] CHEN J H, ZHOU J Y, YE J P. Integrating low-rank and group-sparse structures for robust multi-task learning[C]//Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. San Diego: ACM, 2011: 42–50.
    [10] ARGYRIOU A, EVGENIOU T, PONTIL M. Multi-task feature learning [C]//Conference on Advances in Neural Information Processing Systems. [S.l.]: MIT Press, 2007: 41-48.
    [11] EVGENIOU T, PONTIL M. Regularized multi: task learning[C]//Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle: ACM, 2004: 109-117.
    [12] KIM S, XING E P. Statistical estimation of correlated genome associations to a quantitative trait network[J]. PLoS Genetics, 2009, 5(8): e1000587.1-e1000587.18.
    [13] JACOB L, BACH F, VERT J P. Clustered multi-task learning: a convex formulation[C]//Proceedings of the 21st International Conference on Neural Information Processing Systems. New York: Springer-Verlag, 2008: 745-752.
    [14] ZHANG Y, YEUNG D Y. A convex formulation for learning task relationships in multi-task learning[C]// Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence. [S.l.]: AUAI Press, 2010: 733-742.
    [15] GONALVES A R, VON ZUBEN F J, BANERJEE A. Multi-task sparse structure learning with gaussian copula models[J]. The Journal of Machine Learning Research, 2016, 17(1): 1205-1234.
    [16] ZHOU F, SHUI C J, ABBASI M, et al. Task similarity estimation through adversarial multitask neural network[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(2): 466-480. doi: 10.1109/TNNLS.2020.3028022
    [17] KOKKINOS I. UberNet: training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 5454-5463.
    [18] 赵佳琦,张迪,周勇,等. 基于深度强化学习的遥感图像可解释目标检测方法[J]. 模式识别与人工智能,2021,34(9): 777-786.

    ZHAO Jiaqi, ZHANG Di, ZHOU Yong, et al. Interpretable object detection method for remote sensing image based on deep reinforcement learning[J]. Pattern Recognition and Artificial Intelligence, 2021, 34(9): 777-786.
    [19] MARTIN D R, FOWLKES C C, MALIK J. Learning to detect natural image boundaries using local brightness, color, and texture cues[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5): 530-549. doi: 10.1109/TPAMI.2004.1273918
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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

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