2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于全过程隐私保护的多智能体系统平均一致性

纪良浩 唐少洪 郭兴 解燕

纪良浩, 唐少洪, 郭兴, 解燕. 基于全过程隐私保护的多智能体系统平均一致性. 自动化学报, 2025, 51(6): 1−12 doi: 10.16383/j.aas.c240471
引用本文: 纪良浩, 唐少洪, 郭兴, 解燕. 基于全过程隐私保护的多智能体系统平均一致性. 自动化学报, 2025, 51(6): 1−12 doi: 10.16383/j.aas.c240471
Ji Liang-Hao, Tang Shao-Hong, Guo Xing, Xie Yan. Average consensus in multi-agent systems based on whole-process privacy protection. Acta Automatica Sinica, 2025, 51(6): 1−12 doi: 10.16383/j.aas.c240471
Citation: Ji Liang-Hao, Tang Shao-Hong, Guo Xing, Xie Yan. Average consensus in multi-agent systems based on whole-process privacy protection. Acta Automatica Sinica, 2025, 51(6): 1−12 doi: 10.16383/j.aas.c240471

基于全过程隐私保护的多智能体系统平均一致性

doi: 10.16383/j.aas.c240471 cstr: 32138.14.j.aas.c240471
基金项目: 国家自然科学基金(62276036),重庆市教委科学技术研究项目(KJQN202400627),重庆市自然科学基金创新发展联合基金重点项目(CSTB2024NSCQ-LZX0118),重庆市教委科技重大项目(M202100602), 重庆市教委科学技术研究项目(KJQN202400627)
详细信息
    作者简介:

    纪良浩:重庆邮电大学教授. 2014年获得重庆大学博士学位. 主要研究方向为多智能体系统和智能信息处理. 本文通信作者. E-mail: jilh@cqupt.edu.cn

    唐少洪:重庆邮电大学硕士研究生. 2022年获得沈阳师范大学软件学院学士学位. 主要研究方向为多智能体系统的一致性控制和隐私保护. E-mail: s220201088@stu.cqupt.edu.cn

    郭兴:重庆邮电大学讲师. 2020年获得东南大学博士学位. 主要研究方向为多智能体系统的分布式控制和网络安全控制. E-mail: Guoxing@cqupt.edu.cn

    解燕:重庆邮电大学博士研究生. 2022年获得重庆科技大学硕士学位. 主要研究方向为多智能体系统弹性控制. E-mail: d220201014@stu.cqupt.edu.cn

Average Consensus in Multi-agent Systems Based on Whole-process Privacy Protection

Funds: Supported by National Natural Science Foundation of China (62276036), Innovation and Development Joint Fund Project of Chongqing Natural Science Foundation (CSTB2024NSCQ-LZX0118), Major Project of Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-M202100602), and Science and Technology Research Project of the Chongqing Education Commission (KJQN202400627)
More Information
    Author Bio:

    JI Liang-Hao Professor at Chongqing University of Posts and Telecommunications. He received his Ph.D. degree from Chongqing University in 2014. His research interest covers multi-agent systems and intelligent information processing. Corresponding author of this paper

    TANG Shao-Hong Master student at Chongqing University of Posts and Telecommunications. He received his bachelor degree from the School of Software, Shenyang Normal University in 2022. His research interest covers consensus control of multi-agent systems and privacy protection

    GUO Xing Lecturer at Chongqing University of Posts and Telecommunications. He received his Ph.D. degree from Southeast University in 2020. His research interest covers distributed control and networked secure control of multi-agent systems

    XIE Yan Ph.D. candidate at Chongqing University of Posts and Telecommunications. She received her master degree from Chongqing University of Science and Technology in 2022. Her research interest covers the resilient control of multi-agent systems

  • 摘要: 针对通信网络可能遭受多邻居联合窃听的多智能体系统, 研究其基于全过程隐私保护的平均一致性问题, 具体包括保护智能体的初始状态以及智能体在实现平均一致性整个过程中的实时状态.不同于现有的隐私保护平均一致性算法仅能保护智能体的初始状态且无法抵御联合窃听, 提出基于虚拟子网和非消失扰动的全过程隐私保护平均一致性算法. 在所提算法下, 即使智能体的所有信道都被窃听, 仍然可以实现多智能体系统的平均一致性且智能体的状态可以得到全过程保护. 最后, 通过几个数值仿真实验验证了算法的有效性.
  • 图  1  非联合窃听下的网络示意图

    Fig.  1  Schematic of the network under non-collaborative eavesdropping

    图  4  完全联合窃听下的网络示意图

    Fig.  4  Schematic of the network under full collaborative eavesdropping

    图  2  弱联合窃听下的网络示意图

    Fig.  2  Schematic of the network under weak collaborative eavesdropping

    图  3  强联合窃听下的网络示意图

    Fig.  3  Schematic of the network under strong collaborative eavesdropping

    图  5  算法示例图

    Fig.  5  Schematic diagram of the algorithm example

    图  6  强联合窃听下的简化示意图

    Fig.  6  Simplified schematic under strong collaborative eavesdropping

    图  7  5个节点组成的多智能体系统网络拓扑

    Fig.  7  Network topology of multi-agent system with 5 nodes

    图  8  在式(20)下系统各节点输出值的变化轨迹

    Fig.  8  Trajectories of changes in the output values of each node of the system under (20)

    图  9  在实现方式(20)和(21)下, 智能体的输出轨迹$y_i^{\alpha}(t)$和$\bar{y}_i^{\alpha}(t)$

    Fig.  9  The output trajectories of agents under realizations (20) and (21): $y_i^{\alpha}(t)$ and $\bar{y}_i^{\alpha}(t)$

    图  10  在实现方式(20)和(21)下, 虚拟节点的输出轨迹$y_i^{\beta}(t)$和$\bar{y}_i^{\beta}(t)$

    Fig.  10  The output trajectories of virtual nodes under realizations (20) and (21): $y_i^{\beta}(t)$ and $\bar{y}_i^{\beta}(t)$

    图  11  在实现方式(20)和(21)下, 智能体的实时状态轨迹$x_i^{\alpha}(t)$和$\bar{x}_i^{\alpha}(t)$

    Fig.  11  The real-time state trajectories of agents under realizations (20) and (21): $x_i^{\alpha}(t)$ and $\bar{x}_i^{\alpha}(t)$

    图  12  在实现方式(20)和(21)下, 虚拟节点的状态轨迹$x_i^{\beta}(t)$和$\bar{x}_i^{\beta}(t)$

    Fig.  12  The state trajectories of virtual nodes under realizations (20) and (21): $x_i^{\beta}(t)$ and $\bar{x}_i^{\beta}(t)$

    图  13  在实现方式(20)和(22)下, 节点1的实时状态轨迹$x_i^{\alpha}(t)$和$\bar{\bar{x}}_i^{\alpha}(t)$

    Fig.  13  The real-time state trajectories of node 1 under realizations (20) and (22): $x_i^{\alpha}(t)$ and $\bar{\bar{x}}_i^{\alpha}(t)$

    图  14  在文献[31]的方法下, 每个节点的输出轨迹$y_{-i}(t)$

    Fig.  14  The output trajectory $y_{-i}(t) $ of each node under the method of [31]

    图  15  $Z_2(t)$在本文和文献[31]下的轨迹

    Fig.  15  $Z_2(t)$ trajectories under the method of ours and [31]

  • [1] Chung Y F, Kia S S. Dynamic active average consensus. IEEE Control Systems Letters, 2020, 5(4): 1177−1182
    [2] Hassani H, Razavi-Far R, Saif M, Herrera-Viedma E. Consensus-based decision support model and fusion architecture for dynamic decision making. Information Sciences, 2022, 597: 86−104 doi: 10.1016/j.ins.2022.03.040
    [3] Du Y H, Hao T, Hui Y, Srdjan L. Accurate consensus-based distributed averaging with variable time delay in support of distributed secondary control algorithms. IEEE Transactions on Smart Grid, 2020, 11(4): 2918−2928 doi: 10.1109/TSG.2020.2975752
    [4] Alsaadi F E, Wang Z D, Wang D, Alsaadi F E, Alsaade F W. Recursive fusion estimation for stochastic discrete time-varying complex networks under stochastic communication protocol: The state-saturated case. Information Fusion, 2020, 60: 11−19 doi: 10.1016/j.inffus.2020.01.012
    [5] Zhang K, Li Z J, Wang Y Q, Louati A, Chen J. Privacy-preserving dynamic average consensus via state decomposition: Case study on multi-robot formation control. Automatica, 2022, 139: Article No. 110182
    [6] Dong Y C, Zha Q B, Zhang H J, Kou G, Fujita H, Chiclana F, et al. Consensus reaching in social network group decision making: Research paradigms and challenges. Knowledge-Based Systems, 2018, 162: 3−13 doi: 10.1016/j.knosys.2018.06.036
    [7] Yi D, Yu S Y. Rendezvous with connectivity preservation problem of linear multiagent systems via parallel event-triggered control strategies. IEEE Transactions on Cybernetics, 2020, 52(5): 2725−2734
    [8] Maity D, Tsiotras P. Multiagent consensus subject to communication and privacy constraints. IEEE Transactions on Control of Network Systems, 2021, 9(2): 943−955
    [9] Wang Z Q, Ma M L, Zhou Q, Xiong L Y, Wang L L, Wang J M, et al. A privacy-preserving distributed control strategy in islanded AC microgrids. IEEE Transactions on Smart Grid, 2022, 13(5): 3369−3382 doi: 10.1109/TSG.2022.3171267
    [10] Yao A C. Protocols for secure computations. In: Proceedings of the 23rd Annual Symposium on Foundations of Computer Science. IEEE, 1982.160−164
    [11] Shamir A. How to share a secret. Communications of the ACM, 1979, 22(11): 612−613 doi: 10.1145/359168.359176
    [12] Chaum D, Crépeau C, Damgard I. Multiparty unconditionally secure protocols. In: Proceedings of the 20th Annual ACM Symposium on Theory of Computing. Berlin, Germany: Springer, 1988. 11−19
    [13] Nozari E, Tallapragada P, Cortés J. Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design. Automatica, 2017, 81: 221−231 doi: 10.1016/j.automatica.2017.03.016
    [14] Ruan M H, Gao H, Wang Y Q. Secure and privacy-preserving consensus. IEEE Transactions on Automatic Control, 2019, 64(10): 4035−4049 doi: 10.1109/TAC.2019.2890887
    [15] Mo Y L, Murray R M. Privacy preserving average consensus. IEEE Transactions on Automatic Control, 2016, 62(2): 753−765
    [16] Wang Y Q. Privacy-preserving average consensus via state decomposition. IEEE Transactions on Automatic Control, 2019, 64(11): 4711−4716 doi: 10.1109/TAC.2019.2902731
    [17] Ramos G, Pequito S. Designing communication networks for discrete-time consensus for performance and privacy guarantees. Systems Control Letters, 2023, 180: Article No. 105608
    [18] Gao L, Deng S J, Ren W. Differentially private consensus with an event-triggered mechanism. IEEE Transactions on Control of Network Systems, 2018, 6(1): 60−71
    [19] Fiore D, Russo G. Resilient consensus for multi-agent systems subject to differential privacy requirements. Automatica, 2019, 106: 18−26 doi: 10.1016/j.automatica.2019.04.029
    [20] Hadjicostis C N, Domínguez-García A D. Privacy-preserving distributed averaging via homomorphically encrypted ratio consensus. IEEE Transactions on Automatic Control, 2020, 65(9): 3887−3894 doi: 10.1109/TAC.2020.2968876
    [21] Altafini C. A system-theoretic framework for privacy preservation in continuous-time multiagent dynamics. Automatica, 2020, 122: Article No. 109253
    [22] Xiong Y, Li Z K. Privacy-preserved average consensus algorithms with edge-based additive perturbations. Automatica, 2022, 140: Article No. 110223
    [23] Zhang J, Lu J Q, Lou J G. Privacy-preserving average consensus via finite time-varying transformation. IEEE Transactions on Network Science and Engineering, 2022, 9(3): 1756−1764 doi: 10.1109/TNSE.2022.3151380
    [24] Manitara N E, Hadjicostis C N. Privacy-preserving asymptotic average consensus. In: Proceedings of the European Control Conference Zurich Switzerland: IEEE, 2013. 760−765
    [25] Charalambous T, Manitara N E, Hadjicostis C N. Privacy-preserving average consensus over digraphs in the presence of time delays. In: Proceedings of the 57th Annual Allerton Conference on Communication, Control, and Computing. Monticello, USA: IEEE, 2019. 238−245
    [26] Rezazadeh N, Kia S S. A study of privacy preservation in average consensus algorithm via deterministic obfuscation signals. IEEE Transactions on Control of Network Systems, 2023, 11(1): 534−546
    [27] Ye F, Cao X H, Chow M Y, Cai L. Privacy-preserving average consensus: fundamental analysis and a generic framework design. IEEE Transactions on Information Theory, 2024, 70(4): 2870−2885 doi: 10.1109/TIT.2024.3370311
    [28] 应晨铎, 伍益明, 徐明, 郑宁, 何熊熊. 欺骗攻击下具备隐私保护的多智能体系统均值趋同控制. 自动化学报, 2023, 49(2): 425−436

    Ying Chen-Duo, Wu Yi-Ming, Xu Ming, Zheng Ning, He Xiong-Xiong. Privacy-preserving average consensus control for multi-agent systems under deception attacks. Acta Automatica Sinica, 2023, 49(2): 425−436
    [29] Zhang J, Lu J, Liang J, Shi K. Privacy-preserving average consensus in multi-agent systems via partial information transmission. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 53(5): 2781−2791
    [30] Ramos G, Aguiarz A P, Karx S, Pequito S. Privacy preserving average consensus through network augmentation. IEEE Transactions on Automatic Control, 2024, 69(10): 6907−6919 doi: 10.1109/TAC.2024.3383795
    [31] Zhang J, Lu J Q, Liang J L, Zhong J. Average consensus of whole-process privacy protection: A scale parameter method. Information Fusion, 2024, 107: Article No. 102312
    [32] Wang Z Q, Wang J, Scala M L, Xiong L Y. Real-time privacy-preserving average consensus and its application to secondary control for AC microgrid. IEEE Transactions on Industrial Informatics, 2024, 20(7): 9655−9669 doi: 10.1109/TII.2024.3381645
    [33] Olfati-Saber R, Murray R M. Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 2004, 49(9): 1520−1533 doi: 10.1109/TAC.2004.834113
  • 加载中
计量
  • 文章访问数:  82
  • HTML全文浏览量:  41
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-07-03
  • 录用日期:  2025-01-17
  • 网络出版日期:  2025-05-16

目录

    /

    返回文章
    返回