面向无线边缘网络的分层Stackelberg博弈群体激励方法

康海燕; 冀珊珊

doi:10.12263/DZXB.20231158

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面向无线边缘网络的分层Stackelberg博弈群体激励方法

学术论文 | 更新时间：2025-12-24

- 面向无线边缘网络的分层Stackelberg博弈群体激励方法
- Hierarchical Stackelberg Game Swarm Learning Incentive Method for Wireless Edge Network
- 电子学报 2024年52卷第7期页码：2382-2392
- 作者机构：
  
  北京信息科技大学信息管理学院，北京 100192
- 作者简介：
  
  [ "康海燕男，1971年生，河北石家庄人，博士，教授，北京信息科技大学信息管理学院副院长，主要研究方向为网络安全、隐私计算等. E-mail: kanghaiyan@126.com" ]
  [ "冀珊珊女，1999年生，河北保定人，北京信息科技大学网络空间安全专业在读硕士研究生，主要研究方向为网络安全与隐私保护等. E-mail: jishanshan022@126.com" ]
- 基金信息：
  
  国家社科基金(21BTQ079);教育部人文社科基金(20YJAZH046);国家自然科学基金(61370139)
- DOI：10.12263/DZXB.20231158
  中图分类号： TP391.9;TN929.52
- 收稿：2023-12-12，
  
  修回：2024-04-08，
  
  纸质出版：2024-07-25
- 稿件说明：
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康海燕, 冀珊珊. 面向无线边缘网络的分层Stackelberg博弈群体激励方法[J]. 电子学报, 2024, 52(07): 2382-2392.

KANG Hai-yan, JI Shan-shan. Hierarchical Stackelberg Game Swarm Learning Incentive Method for Wireless Edge Network[J]. Acta Electronica Sinica, 2024, 52(07): 2382-2392.
康海燕, 冀珊珊. 面向无线边缘网络的分层Stackelberg博弈群体激励方法[J]. 电子学报, 2024, 52(07): 2382-2392. DOI：10.12263/DZXB.20231158

KANG Hai-yan, JI Shan-shan. Hierarchical Stackelberg Game Swarm Learning Incentive Method for Wireless Edge Network[J]. Acta Electronica Sinica, 2024, 52(07): 2382-2392. DOI：10.12263/DZXB.20231158

摘要

现有分布式机器学习模型的相关激励机制大多基于单层服务器架构，难以适应当前异构无线计算场景，同时存在计算资源分配不平衡、通信成本高昂等问题.针对上述问题，创新地提出一种面向无线边缘网络的分层Stackelberg博弈群体激励方法（Hierarchical Stackelberg game Swarm Learning Incentive method for wireless edge network，HSISL），创新地将Stackelberg博弈机制引入群体学习模型中，依据各参与方性能差异，云端聚合平台、边缘簇节点、边缘计算节点三方进行动态博弈，通过双定价公平激励过程，共同制定个性化分层资源分配策略，得到模型训练的最优纳什解，有效引导边缘计算模型进行正向加速.通过理论与实验分析，HSISL能够有效提升模型公平性与训练效率，其在MNIST数据集上的准确率可达96.06%.

Abstract

In today’s rapidly evolving landscape of distributed machine learning

conventional data incentive solutions often fall short due to their reliance on simplistic single-server architectures

in addition

as computing environments become increasingly complex

particularly within the context of heterogeneous wireless networks

these traditional approaches struggle to meet the dynamic computational demands such as unbalanced resource allocation and exorbitant communication costs. In response to the above dilemma

this paper innovatively proposes a hierarchical Stackelberg game swarm learning incentive method for wireless edge network (HSISL). This paper innovatively introduces the Stackelberg game mechanism into the swarm learning. Based on the performance differences of each computing terminal

the cloud aggregation platform

edge cluster nodes

and edge computing nodes conduct dynamic games and jointly formulate personalized hierarchical resource allocation strategies through the fair incentive process of dual pricing

which can effectively guide the edge computing model to accelerate forward. Through theoretical and experimental analysis

the HSISL method can obtain the optimal incentive Nash equilibrium solution for model training. Compared with other incentive methods

the HSISL method can effectively improve the fairness of the model. With training efficiency

its accuracy on the MNIST data set can reach 96.06%.

关键词

Keywords

references

WARNAT-HERRESTHAL S , SCHULTZE H , SHASTRY K L , et al . Swarm learning for decentralized and confidential clinical machine learning [J ] . Nature , 2021 , 594 ( 7862 ): 265 - 270 .

KALRA S , WEN J F , CRESSWELL J C , et al . Decentralized federated learning through proxy model sharing [J ] . Nature Communications , 2023 , 14 ( 2899 ): 1 - 10 .

BO Z H , GUO Y C , LYU J H , et al . Relay learning: A physically secure framework for clinical multi-site deep learning [J ] . NPJ Digital Medicine , 2023 , 6 ( 204 ): 1 - 11 .

LI W T , KIM M , ZHANG K , et al . COLLAGENE enables privacy-aware federated and collaborative genomic data analysis [J ] . Genome Biology , 2023 , 24 ( 204 ): 1 - 38 .

WANG Z , LI X H , WU T H , et al . A credibility-aware swarm-federated deep learning framework in Internet of vehicles [J ] . Digital Communications and Networks , 2024 , 10 ( 1 ): 150 - 157 .

LIU Y B , HUO L J , WU J , et al . Swarm learning-based dynamic optimal management for traffic congestion in 6G-driven intelligent transportation system [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 7 ): 7831 - 7846 .

SALDANHA O L , QUIRKE P , WEST N P , et al . Swarm learning for decentralized artificial intelligence in cancer histopathology [J ] . Nature Medicine , 2022 , 28 ( 6 ): 1232 - 1239 .

DONG X S , SARKER S , QIAN L J . Integrating human-in-the-loop into swarm learning for decentralized fake news detection [C ] // 2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA) . Piscataway : IEEE , 2022 : 46 - 53 .

SALDANHA O L , MUTI H S , GRABSCH H I , et al . Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning [J ] . Gastric Cancer , 2023 , 26 ( 2 ): 264 - 274 .

KANG H Y , JI Y R , ZHANG S X . Enhanced privacy preserving for social networks relational data based on personalized differential privacy [J ] . Chinese Journal of Electronics , 2022 , 31 ( 4 ): 741 - 751 .

陈颢瑜 , 李浥东 , 张洪磊 , 等 . 面向可信联邦学习公平性的研究综述 [J ] . 电子学报 , 2023 , 51 ( 10 ): 2985 - 3010 .

CHEN H Y , LI Y D , ZHANG H L , et al . Fairness in trustworthy federated learning: A survey [J ] . Acta Electronica Sinica , 2023 , 51 ( 10 ): 2985 - 3010 . (in Chinese)

王勇 , 李国良 , 李开宇 . 联邦学习贡献评估综述 [J ] . 软件学报 , 2023 , 34 ( 3 ): 1168 - 1192 .

WANG Y , LI G L , LI K Y . Survey on contribution evaluation for federated learning [J ] . Journal of Software , 2023 , 34 ( 3 ): 1168 - 1192 . (in Chinese)

ZHAO N , WAN Q X , CHEN J L , et al . Dynamic incentive mechanism in mobile crowdsourcing networks by combining reputation and contract theory [J ] . International Journal of Distributed Sensor Networks , 2022 , 18 ( 6 ): 1 - 14 .

LU J F , LIU H B , JIA R H , et al . Toward personalized federated learning via group collaboration in IIoT [J ] . IEEE Transactions on Industrial Informatics , 2023 , 19 ( 8 ): 8923 - 8932 .

HE W J , YAO H P , MAI T L , et al . Three-stage stackelberg game enabled clustered federated learning in heterogeneous UAV swarms [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 7 ): 9366 - 9380 .

贾云健 , 黄宇 , 梁靓 , 等 . 基于主从博弈的分层联邦学习激励机制研究 [J ] . 电子与信息学报 , 2023 , 45 ( 4 ): 1366 - 1373 .

JIA Y J , HUANG Y , LIANG L , et al . Research on hierarchical federated learning incentive mechanism based on master-slave game [J ] . Journal of Electronics & Information Technology , 2023 , 45 ( 4 ): 1366 - 1373 . (in Chinese)

周赞 , 张笑燕 , 杨树杰 , 等 . 面向联邦算力网络的隐私计算自适激励机制 [J ] . 计算机学报 , 2023 , 46 ( 12 ): 2705 - 2725 .

ZHOU Z , ZHANG X Y , YANG S J , et al . Adaptive incentive mechanism for privacy computing in federated compute first networks [J ] . Chinese Journal of Computers , 2023 , 46 ( 12 ): 2705 - 2725 . (in Chinese)

LI C L , SONG M Y , LUO Y L . Federated learning based on Stackelberg game in unmanned-aerial-vehicle-enabled mobile edge computing [J ] . Expert Systems with Applications , 2024 , 235 ( 121023 ): 1 - 15 .

CHEN Y L , ZHOU H , LI T , et al . Multifactor incentive mechanism for federated learning in IoT: A stackelberg game approach [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 24 ): 21595 - 21606 .

HUANG Z H . Incentive mechanism design for joint resource allocation in post-quantum blockchain-based federated learning [C ] // 2023 International Conference on Electronics and Devices, Computational Science (ICEDCS) . Piscataway : IEEE , 2023 : 447 - 451 .

LIU Y , LIU P , JING W P , et al . PD2S: A privacy-preserving differentiated data sharing scheme based on blockchain and federated learning [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 24 ): 21489 - 21501 .

FREDRIKSON M , JHA S , RISTENPART T . Model inversion attacks that exploit confidence information and basic countermeasures [C ] // Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . New York : ACM , 2015 : 1322 - 1333 .

KHAN L U , PANDEY S R , TRAN N H , et al . Federated learning for edge networks: Resource optimization and incentive mechanism [J ] . IEEE Communications Magazine , 2020 , 58 ( 10 ): 88 - 93 .

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