激励驱动的联邦学习研究综述：隐私与安全

迟欢欢; 熊平; 刘恒竹; 马霄; 朱天清

doi:10.11959/j.issn.2096-109x.2025035

您当前的位置：

首页 >

文章列表页 >

激励驱动的联邦学习研究综述：隐私与安全

综述 | 更新时间：2025-06-30

- 激励驱动的联邦学习研究综述：隐私与安全
- Survey on incentive-driven federated learning: privacy and security
- 网络与信息安全学报 2025年11卷第3期页码：19-44
- 作者机构：
  
  1.中南财经政法大学信息工程学院，湖北武汉 430073
  2.澳门城市大学数据科学学院，澳门 999078
- 作者简介：
  
  [ "迟欢欢（1995- ），女，河南周口人，中南财经政法大学博士生，主要研究方向为联邦学习、隐私保护、激励机制。" ]
  [ "熊平（1974- ），男，湖北荆州人，中南财经政法大学教授、博士生导师，主要研究方向为机器学习、信息安全、联邦学习、隐私保护。" ]
  [ "刘恒竹（1996- ），女，河南平顶山人，中南财经政法大学博士生，主要研究方向为人工智能、隐私保护、机器遗忘。" ]
  [ "马霄（1989- ），女，河南新乡人，中南财经政法大学副教授，主要研究方向为推荐系统、联邦学习、图算法、人工智能。" ]
  [ "朱天清（1979- ），女，湖北武汉人，澳门城市大学教授、博士生导师，主要研究方向为信息安全、人工智能安全、数据隐私保护。" ]
- 基金信息：
  
  高等学校学科创新引智基地(B21038);湖北省自然科学基金(2025AFC108;2024AFB957)
- DOI：10.11959/j.issn.2096-109x.2025035
  中图分类号： TP181
- 收稿：2025-01-27，
  
  修回：2025-04-16，
  
  纸质出版：2025-06-25
- 稿件说明：
移动端阅览
迟欢欢, 熊平, 刘恒竹, 等. 激励驱动的联邦学习研究综述:隐私与安全[J]. 网络与信息安全学报, 2025, 11(3): 19-44.

Format: CHI H H, XIONG P, LIU H Z, et al. Survey on incentive-driven federated learning: privacy and security[J]. Chinese Journal of Network and Information Security, 2025, 11(3): 19-44.
迟欢欢, 熊平, 刘恒竹, 等. 激励驱动的联邦学习研究综述:隐私与安全[J]. 网络与信息安全学报, 2025, 11(3): 19-44. DOI： 10.11959/j.issn.2096-109x.2025035.

Format: CHI H H, XIONG P, LIU H Z, et al. Survey on incentive-driven federated learning: privacy and security[J]. Chinese Journal of Network and Information Security, 2025, 11(3): 19-44. DOI： 10.11959/j.issn.2096-109x.2025035.

摘要

联邦学习使多个数据持有者能够在不泄露本地数据的前提下协同完成机器学习任务，而鼓励参与者加入联邦学习并提供高质量数据是其成功的关键因素之一。然而，联邦学习在实际应用中面临隐私泄露和安全威胁双重挑战。一方面，恶意参与者通过主动攻击干扰联邦学习的进程和结果，破坏其有效性和鲁棒性；另一方面，被动攻击造成的隐私泄露风险对参与者加入联邦学习的主观意愿造成负面影响，使激励机制的实施变得更加困难。近年来，学术界从隐私保护和安全防御的角度对激励驱动的联邦学习进行了广泛研究，旨在从安全性和公平性兼顾的视角，为联邦学习提供整体解决方案。首先，介绍了激励驱动联邦学习面临的各种被动攻击和主动攻击，深入分析这些攻击引发的隐私泄露风险和安全威胁。然后，分别从隐私保护和安全防御两个方面对激励驱动的联邦学习研究进行综述和分析。在隐私保护方面，着重介绍了差分隐私和同态加密两种技术在激励驱动联邦学习中的应用；在安全防御方面，重点梳理了结合激励机制来防御投毒攻击和搭便车攻击的各种方法。最后，讨论了激励驱动的联邦学习在应对隐私和安全攻击方面面临的挑战，并展望了这一领域未来的研究方向。

Abstract

Federated learning was enabled to allow multiple data holders to jointly complete machine learning tasks without disclosing local data. Incentivizing participants to engage in federated learning and contribute high-quality data was identified as one of the key factors for its success. However

federated learning was found to face challenges in both privacy leakage and security threats in practice. On the one hand

malicious participants were observed to launch active attacks to disrupt the process and results of federated learning

compromising its effectiveness and robustness. On the other hand

privacy leakage risks caused by passive attacks were shown to negatively impact participants’ willingness to join federated learning

making the implementation of incentive mechanisms more difficult. In recent years

extensive research was conducted by the community on incentive-driven federated learning from the perspectives of privacy preservation and security defense

aiming to provide comprehensive solutions that balance security and fairness for federated learning. First

various passive and active attacks faced by incentive-driven federated learning were introduced

and the privacy leakage risks and security threats posed by these attacks were analyzed. Subsequently

a comprehensive review and analysis of incentive-driven federated learning research were provided from the perspectives of privacy preservation and security defense. In terms of privacy preservation

the application of differential privacy and homomorphic encryption technologies in incentive-driven federated learning was focused on. In terms of security defense

various methods that combine incentive mechanisms to defend against poisoning attacks and free-riding attacks were specifically outlined. Finally

the challenges still faced by incentive-driven federated learning in addressing privacy leakage and security attacks were discussed

and potential future research directions in this field were explored.

关键词

Keywords

references

ALEDHARI M , RAZZAK R , PARIZI R M , et al . Federated learning: a survey on enabling technologies, protocols, and applications [J ] . IEEE Access , 2020 , 8 : 140699 - 140725 .

LUO S , CHEN X , WU Q , et al . HFEL: joint edge association and resource allocation for cost-efficient hierarchical federated edge learning [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 10 ): 6535 - 6548 .

ALI A M , ILAHI I , QAYYUM A , et al . Incentive-driven federated learning and associated security challenges: a systematic review [C ] // Proceedings of the 2021 ACM Conference on Incentive-Driven Federated Learning . New York : ACM Press , 2021 .

ZHAN Y , ZHANG J , HONG Z , et al . A survey of incentive mechanism design for federated learning [J ] . IEEE Transactions on Emerging Topics in Computing , 2021 , 10 ( 2 ): 1035 - 1044 .

LIU Z , CHEN Y , ZHAO Y , et al . Contribution-aware federated learning for smart healthcare [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Vancouver : AAAI Press , 2022 : 12396 - 12404 .

GUO W , WANG Y , JIANG P . Incentive mechanism design for federated learning with stackelberg game perspective in the industrial scenario [J ] . Computers & Industrial Engineering , 2023 , 184 : 109592 .

LIM W Y B , XIONG Z , KANG J , et al . When information freshness meets service latency in federated learning: a task-aware incentive scheme for smart industries [J ] . IEEE Transactions on Industrial Informatics , 2022 , 18 ( 1 ): 457 - 466 .

SONG C , RISTENPART T , SHMATIKOV V . Machine learning models that remember too much [C ] // Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . New York : ACM Press , 2017 : 587 - 601 .

ZHANG J , CHEN J , WU D , et al . Poisoning attack in federated learning using generative adversarial nets [C ] // Proceedings of the 18th IEEE International Conference on Trust . Piscataway : IEEE Press , 2019 : 374 - 380 .

ZHU L , LIU Z , SONG H . Deep leakage from gradients [C ] // Proceedings of the 33rd International Conference on Neural Information Processing Systems . 2020 : 14774 - 14784 .

乐紫莹 , 陈珂 , 寿黎但 , 等 . 基于梯度放大的联邦学习激励欺诈攻击与防御 [J ] . 软件学报 , 2025 , 36 ( 5 ): 2254 - 2269 .

YUE Z Y , CHEN K , SHOU L D , et al . Reward fraud attack and defense for federated learning based on gradient scale-up [J ] . Journal of Software , 2025 , 36 ( 5 ): 2254 - 2269 .

ZHANG C , XIE Y , BAI H , et al . A survey on federated learning [J ] . Knowledge-Based Systems , 2021 , 216 : 106775 .

YANG Q , LIU Y , CHEN T , et al . Federated machine learning: concept and applications [J ] . ACM Transactions on Intelligent Systems and Technology , 2019 , 10 ( 2 ): 1 - 19 .

MCMAHAN B , MOORE E , RAMAGE D , et al . Communication-efficient learning of deep networks from decentralized data [C ] // Proceedings of Artificial Intelligence and Statistics . New York : PMLR , 2017 : 1273 - 1282 .

HUANG Y , CHU L , ZHOU Z , et al . Personalized cross-silo federated learning on non-IID data [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Hong Kong : AAAI Press , 2021 : 7865 - 7873 .

WANG H , YUROCHKIN M , SUN Y , et al . Federated learning with matched averaging [J ] . arXiv preprint arXiv: 2002.06440 , 2020 .

KARIMIREDDY S P , KALE S , MOHRI M , et al . SCAFFOLD: stochastic controlled averaging for federated learning [C ] // Proceedings of the 37th International conference on machine learning . New York : PMLR , 2020 : 5132 - 5143 .

GAO J , ZHAO L , SHEN X . Network utility maximization based on an incentive mechanism for truthful reporting of local information [J ] . IEEE Transactions on Vehicular Technology , 2018 , 67 ( 8 ): 7523 - 7537 .

ZENG R , ZENG C , WANG X , et al . A comprehensive survey of incentive mechanism for federated learning [J ] . arXiv preprint arXiv: 2106.15406 , 2021 .

SOLAN E . Stochastic games [C ] // Proceedings of the Meyers RA . 2012 : 3064 - 3074 .

JIA R , DAO D , WANG B , et al . Towards efficient data valuation based on the shapley value [C ] // Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics . New York : PMLR , 2019 : 1167 - 1176 .

SARIKAYA Y , ERCETIN O . Motivating workers in federated learning: a stackelberg game perspective [J ] . IEEE Networking Letters , 2019 , 2 ( 1 ): 23 - 27 .

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 .

WEMPE B . Contract theory [M ] . Berlin : Springer-Verlag , 2020 .

ERASLAN B , GOZUPEK D , ALAGOZ F . An auction theory based algorithm for throughput maximizing scheduling in centralized cognitive radio networks [J ] . IEEE Communications Letters , 2011 , 15 ( 7 ): 734 - 736 .

CHANDRASHEKAR T S , NARAHARI Y , ROSA C H , et al . Auction-based mechanisms for electronic procurement [J ] . IEEE Transactions on Automation Science and Engineering , 2007 , 4 ( 3 ): 297 - 321 .

FENG J , WU Y , SUN H , et al . Panther: practical secure two-party neural network inference [J ] . IEEE Transactions on Information Forensics and Security , 2025 , 20 : 1149 - 1162 .

LYU L J , YU H , MA X J , et al . Privacy and robustness in federated learning: attacks and defenses [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2024 , 35 ( 7 ): 8726 - 8746 .

陈晋音 , 李荣昌 , 黄国瀚 , 等 . 纵向联邦学习方法及其隐私和安全综述 [J ] . 网络与信息安全学报 , 2023 , 9 ( 2 ): 1 - 20 .

CHEN J Y , LI R C , HUANG G H , et al . Survey on vertical federated learning: algorithm, privacy and security [J ] . Chinese Journal of Network and Information Security , 2023 , 9 ( 2 ): 1 - 20 .

顾育豪 , 白跃彬 . 联邦学习模型安全与隐私研究进展 [J ] . 软件学报 , 2023 , 34 ( 6 ): 2833 - 2864 .

GU Y H , BAI Y B . Survey on security and privacy of federated learning models [J ] . Journal of Software , 2023 , 34 ( 6 ): 2833 - 2864 .

NASR M , SHOKRI R , HOUMANSADR A . Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning [C ] // Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP) . Piscataway : IEEE Press , 2019 : 739 - 753 .

KRICHEN M . Generative adversarial networks [C ] // Proceedings of the 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) . 2023 : 1 - 7 .

王坤庆 , 刘婧 , 李晨 , 等 . 联邦学习安全威胁综述 [J ] . 信息安全研究学报 , 2022 , 8 ( 3 ): 223 - 234 .

WANG K Q , LIU J , Li C , et al . A survey on threats to federated learning [J ] . Journal of Information Security Research , 2022 , 8 ( 3 ): 223 - 234 .

SHEN M , WANG H , ZHANG B , et al . Exploiting unintended property leakage in blockchain-assisted federated learning for intelligent edge computing [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 4 ): 2265 - 2275 .

MELIS L , SONG C , DE CRISTOFARO E , et al . Exploiting unintended feature leakage in collaborative learning [C ] // Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP) . Piscataway : IEEE Press , 2019 : 691 - 706 .

SONG M , WANG Z , ZHANG Z , et al . Analyzing user-level privacy attack against federated learning [J ] . IEEE Journal on Selected Areas in Communications , 2020 , 38 ( 10 ): 2430 - 2444 .

DRIOUICH I , XU C , NEGLIA G , et al . Local model reconstruction attacks in federated learning and their uses [J ] . arXiv preprint arXiv: 2210.16205 , 2022 .

ZHANG X , LI J , ZHANG J , et al . Data reconstruction from gradient updates in federated learning [C ] // Proceedings of the Machine Learning for Cyber Security: 4th International Conference . Berlin : Springer , 2023 : 586 - 596 .

MOTHUKURI V , PARIZI R M , POURIYEH S , et al . A survey on security and privacy of federated learning [J ] . Future Generation Computer Systems , 2021 , 115 : 619 - 640 .

BAGDASARYAN E , VEIT A , HUA Y , et al . How to backdoor federated learning [C ] // Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics . New York : PMLR , 2020 : 2938 - 2948 .

高莹 , 陈晓峰 , 张一余 , 等 . 联邦学习系统攻击与防御技术研究综述 [J ] . 计算机学报 , 2023 , 46 ( 9 ): 1781 - 1805 .

GAO Y , CHEN X F , ZHANG Y Y , et al . A survey of attack and defense techniques for federated learning systems [J ] . Chinese JournaL of Computers , 2023 , 46 ( 9 ): 1781 - 1805 .

WANG H , SREENIVASAN K , RAJPUT S , et al . Attack of the tails: yes, you really can backdoor federated learning [J ] . Advances in Neural Information Processing Systems , 2020 , 33 : 16070 - 16084 .

XIE C , CHEN M , CHEN P Y , et al . CRFL: certifiably robust federated learning against backdoor attacks [C ] // Proceedings of International Conference on Machine Learning . New York : PMLR , 2021 : 11372 - 11382 .

TOLPEGIN V , TRUEX S , GURSOY M E , et al . Data poisoning attacks against federated learning systems [C ] // Proceedings of Computer Security-ESORICS 2020: 25th European Symposium on Research in Computer Security . 2020 : 480 - 501 .

LIM W Y B , LUONG N C , HOANG D T , et al . Federated learning in mobile edge networks: a comprehensive survey [J ] . IEEE Communications Surveys & Tutorials , 2020 , 22 ( 3 ): 2031 - 2063 .

FANG M , CAO X , JIA J , et al . Local model poisoning attacks to Byzantine-Robust federated learning [C ] // Proceedings of the 29th USENIX Security Symposium (USENIX Security 20). 2020 : 1605 - 1622 .

BHAGOJI A N , CHAKRABORTY S , MITTAL P , et al . Analyzing federated learning through an adversarial lens [C ] // Proceedings of International Conference on Machine Learning . 2019 : 634 - 643 .

FARHADKHANI S , GUERRAOUI R , VILLEMAUD O . An equivalence between data poisoning and Byzantine gradient attacks [C ] // Proceedings of International Conference on Machine Learning . 2022 : 6284 - 6323 .

WEN J , ZHANG Z , LAN Y , et al . A survey on federated learning: challenges and applications [J ] . International Journal of Machine Learning and Cybernetics , 2023 , 14 ( 2 ): 513 - 535 .

BLANCHARD P , MHAMDI E M E , GUERRAOUI R , et al . Machine learning with adversaries: Byzantine tolerant gradient descent [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . 2017 : 118 - 128 .

LI Y , LI Y , WU B , et al . Invisible backdoor attack with sample-specific triggers [C ] // Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . 2021 : 10 - 17 .

SUN Z , KAIROUZ P , SURESH A T , et al . Can you really backdoor federated learning [J ] . arXiv preprint arXiv: 1911.07963 , 2019 .

LIN J , DU M , LIU J . Free-riders in federated learning: attacks and defenses [J ] . arXiv preprint arXiv: 1911.12560 , 2019 .

FRABONI Y , VIDAL R , LORENZI M . Free-rider attacks on model aggregation in federated learning [C ] // International Conference on Artificial Intelligence and Statistics . 2021 : 1846 - 1854 .

FENG J , YANG L T , ZHU Q , et al . Privacy-preserving tensor decomposition over encrypted data in a federated cloud environment [J ] . IEEE Transactions on Dependable and Secure Computing , 2020 , 17 ( 4 ): 857 - 868 .

DWORK C , MCSHERRY F , NISSIM K , et al . Calibrating noise to sensitivity in private data analysis [C ] // Proceedings of the Theory of Cryptography . 2006 : 3876 .

HAEBERLEN A , PIERCE B C , NARAYAN A . Differential privacy under fire [C ] // Proceedings of the 20th USENIX Conference on Security . Berkeley : USENIX Association , 2011 : 33 .

HU R , GUO Y , LI H , et al . Personalized federated learning with differential privacy [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 10 ): 9530 - 9539 .

LEE J , KIM D , NIYATO D . Market analysis of distributed learning resource management for internet of things: a game-theoretic approach [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 9 ): 8430 - 8439 .

HU R , GONG Y . Trading data for learning: incentive mechanism for on-device federated learning [C ] // Proceedings of the 2020 IEEE Global Communications Conference . Piscataway : IEEE Press , 2020 : 1 - 6 .

BUN M , STEINKE T . Concentrated differential privacy: simplifications, extensions, and lower bounds [C ] // Theory of Cryptography Conference . 2016 : 635 - 658 .

LIU Y , LIU P , JING W , 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 .

HUANG G , WU Q , SUN P , et al . Collaboration in federated learning with differential privacy: a stackelberg game analysis [J ] . IEEE Transactions on Parallel and Distributed Systems , 2024 , 35 ( 3 ): 455 - 469 .

LI M , TIAN Y , ZHANG J , et al . IMFL: an incentive mechanism for federated learning with personalized protection [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 13 ): 23862 - 23877 .

YUAN W , WANG X . QI-DPFL: quality-aware and incentive-boosted federated learning with differential privacys [J ] . arXiv preprint arXiv: 2404.08261 , 2024 .

LIU T , DI B , AN P , et al . Privacy-preserving incentive mechanism design for federated cloud-edge learning [J ] . IEEE Transactions on Network Science and Engineering , 2021 , 8 ( 3 ): 2588 - 2600 .

PAN Q , WU J , BASHIR A K , et al . Joint protection of energy security and information privacy federated learning approach [J ] . IEEE Transactions on Industrial Informatics , 2022 , 18 ( 5 ): 3473 - 3483 .

MAO W , MA Q , LIAO G , et al . Game analysis and incentive mechanism design for differentially private cross-silo federated learning [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 10 ): 9337 - 9351 .

ZHANG L , ZHU T , XIONG P , et al . A robust game-theoretical federated learning framework with joint differential privacy [J ] . IEEE Transactions on Knowledge and Data Engineering , 2023 , 35 ( 4 ): 3333 - 3346 .

YIN L , LIN S , SUN Z , et al . A game-theoretic approach for federated learning: a trade-off among privacy, accuracy and energy [J ] . Digital Communications and Networks , 2024 , 10 ( 2 ): 389 - 403 .

SUN P , CHE H , WANG Z , et al . Pain-FL: personalized privacy-preserving incentive for federated learning [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 12 ): 3805 - 3820 .

WU M , YE D , DING J , et al . Incentivizing differentially private federated learning: a multidimensional contract approach [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 13 ): 10639 - 10651 .

LIN X , WU J , LI J , et al . Heterogeneous differential-private federated learning: trading privacy for utility truthfully [J ] . IEEE Transactions on Dependable and Secure Computing , 2023 , 20 ( 6 ): 5113 - 5129 .

ZHENG S , CAO Y , YOSHIKAWA M , et al . FL-Market: trading private models in federated learning [C ] // Proceedings of the 2022 IEEE International Conference on Big Data . Piscataway : IEEE Press , 2022 : 1525 - 1534 .

YING C , JIN H , WANG X , et al . Double insurance: incentivized federated learning with differential privacy in mobile crowdsensing [C ] // Proceedings of the 2020 International Symposium on Reliable Distributed Systems (SRDS) . Piscataway : IEEE Press , 2020 : 81 - 90 .

WANG Z , LI J , HU J , et al . Towards privacy-driven truthful incentives for mobile crowdsensing under untrusted platform [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 2 ): 1198 - 1212 .

ZHU K , HUANG L , NIE J , et al . Privacy-aware double auction with time-dependent valuation for blockchain-based dynamic spectrum sharing in IoT systems [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 8 ): 6756 - 6768 .

REN K , LIAO G C , MA Q , et al . Differentially private auction design for federated learning with Non-IID data [C ] // Proceedings of the 2022 International Conference on Service Science (ICSS) . 2022 : 305 - 312 .

LI D , GUO Q , YANG C , et al . Trusted data sharing mechanism based on blockchain and federated learning in space-air-ground integrated networks [J ] . Wireless Communications and Mobile Computing , 2022 , 1 : 5338876 .

YANG Y , HU M , ZHOU Y , et al . CSRA: robust incentive mechanism design for differentially private federated learning [J ] . IEEE Transactions on Information Forensics and Security , 2024 , 19 : 892 - 906 .

WANG D , REN J , WANG Z , et al . PrivAim: a dual-privacy preserving and quality-aware incentive mechanism for federated learning [J ] . IEEE Transactions on Computers , 2023 , 72 ( 7 ): 1913 - 1927 .

LIU H , MING Y , WANG C , et al . Flexible selective data sharing with fine-grained erasure in VANETs [J ] . IEEE Transactions on Information Forensics and Security , 2024 , 19 : 9582 - 9597 .

ZHANG C , LI S , XIA J , et al . BatchCrypt: efficient homomorphic encryption for cross-silo federated learning [C ] // Proceedings of the 2020 USENIX Annual Technical Conference . Berkeley : USENIX Association , 2020 : 493 - 506 .

GAMAL T E . A public key cryptosystem and a signature scheme based on discrete logarithms [J ] . IEEE Transactions on Information Theory , 1984 , 31 ( 4 ): 469 - 472 .

PAILLIER P . Public-key cryptosystems based on composite degree residuosity classes [C ] // Proceedings of the International Conference on the Theory and Application of Cryptographic Techniques . Berlin : Springer , 1999 : 223 - 238 .

WENG J , WENG J , ZHANG J , et al . DeepChain: auditable and privacy-preserving deep learning with blockchain-based incentive [J ] . IEEE Transactions on Dependable and Secure Computing , 2021 , 18 ( 5 ): 2438 - 2455 .

周炜 , 王超 , 徐剑 , 等 . 基于区块链的隐私保护去中心化联邦学习模型 [J ] . 计算机研究与发展 , 2022 , 59 ( 11 ): 2423 - 2436 .

ZHOU W , WANG C , XU J , et al . Privacy-preserving and decentralized federated learning model based on the blockchain [J ] . Journal of Computer Research and Development , 2022 , 59 ( 11 ): 2423 - 2436 .

JIA Y , XIONG L , FAN Y , et al . Blockchain-based privacy-preserving multi-tasks federated learning framework [J ] . Connection Science , 2024 , 36 ( 1 ): 2299103 .

ZHAO B , LIU X , CHEN W N , et al . CrowdFL: privacy-preserving mobile crowdsensing system via federated learning [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 8 ): 4607 - 4619 .

CHEN Q , YAO L , WANG X , et al . SecMDGM: federated learning security mechanism based on multi-dimensional auctions [J ] . Sensors , 2022 , 22 ( 23 ): 9434 .

HE Y , LUO M , WU B , et al . A game theory-based incentive mechanism for collaborative security of federated learning in energy blockchain environment [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 24 ): 21294 - 21308 .

CHEON J H , HAN K , KIM A , et al . Bootstrapping for approximate homomorphic encryption [C ] // Proceedings of the Advances in Cryptology -EUROCRYPT 2018 . Berlin : Springer , 2018 .

XIONG R , REN W , ZHAO S , et al . CoPiFL: a collusion-resistant and privacy-preserving federated learning crowdsourcing scheme using blockchain and homomorphic encryption [J ] . Future Generation Computer Systems , 2024 , 156 : 95 - 104 .

DOMINGO-FERRER J , BLANCO-JUSTICIA A , MANJÓN J , et al . Secure and privacy-preserving federated learning via coutility [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 5 ): 3988 - 4000 .

KASYAP H , MANNA A , TRIPATHY S . An efficient blockchain assisted reputation aware decentralized federated learning framework [J ] . IEEE Transactions on Network and Service Management , 2023 , 20 ( 3 ): 2771 - 2782 .

XU X , LYU L . A reputation mechanism is all you need: collaborative fairness and adversarial robustness in federated learning [J ] . arXiv preprint arXiv: 2011.10464 , 2020 .

OUYANG J , WANG W . FedCrowdSensing: incentive mechanism for crowdsensing based on reputation and federated learning [C ] // Proceedings of the 2023 IEEE Symposium on Computers and Communications (ISCC) . Piscataway : IEEE Press , 2023 : 1454 - 1461 .

ZHAO Y , ZHAO J , JIANG L , et al . Privacy-preserving blockchain-based federated learning for IoT devices [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 3 ): 1817 - 1829 .

WANG Z , YAN B , DONG A . Blockchain empowered federated learning for data sharing incentive mechanism [J ] . Procedia Computer Science , 2022 , 202 : 348 - 353 .

YANG X H , LI T C . A blockchain-based federated learning framework for secure aggregation and fair incentives [J ] . Connection Science , 2024 , 36 ( 1 ): 2316018 .

FENG L , YANG Z , GUO S , et al . Two-layered blockchain architecture for federated learning over the mobile edge network [J ] . IEEE Network , 2022 , 36 ( 1 ): 45 - 51 .

KANG J , XIONG Z , NIYATO D , et al . Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 6 ): 10700 - 10714 .

LIU Y , LI K , ZHANG Y , et al . A novel reputation computation model based on subjective logic for mobile ad hoc networks [C ] // Proceedings of the 2009 Third International Conference on Network and System Security . Piscataway : IEEE Press , 2009 : 294 - 301 .

ZHANG X , HUA Y , QIAN C . Secure decentralized learning with blockchain [C ] // Proceedings of the 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS) . Piscataway : IEEE Press , 2023 : 116 - 124 .

XU G , ZHOU Z , DONG J , et al . A blockchain-based federated learning scheme for data sharing in industrial internet of things [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 24 ): 21467 - 21478 .

REHMAN M H , SALAH K , DAMIANI E , et al . Towards blockchain-based reputation-aware federated learning [C ] // Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . Piscataway : IEEE Press , 2020 : 183 - 188 .

WU Y , CHEN G , LIU Y , et al . TBFL: a trusted blockchain-based federated learning system [C ] // Proceedings of the 2022 IEEE 24th Int Conf on High Performance Computing & Communications . Piscataway : IEEE Press , 2022 : 311 - 318 .

ZHAO S , PU J , FU X , et al . Byzantine-robust federated learning with ensemble incentive mechanism [J ] . Future Generation Computer Systems , 2024 , 159 : 272 - 283 .

ZENG H , LI J , LOU J , et al . BSR-FL: an efficient Byzantine-robust privacy-preserving federated learning framework [J ] . IEEE Transactions on Computers , 2024 , 73 ( 8 ): 2096 - 2110 .

LI H , SHI K . Incentive mechanism for federated learning participants based on statistical analysis features [C ] // Proceedings of the 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE 2023) . Piscataway : IEEE Press , 2023 : 158 - 161 .

CHEN X , LAN P , ZHOU Z , et al . Toward federated learning with Byzantine and inactive users: a game theory approach [J ] . IEEE Access , 2023 , 11 : 34138 - 34149 .

HE Q , TIAN Y , WANG S , et al . RRFL: a rational and reliable federated learning incentive framework for mobile crowdsensing [J ] . Journal of King Saud University-Computer and Information Sciences , 2024 , 36 ( 3 ): 101977 .

GAO L , LI L , CHEN Y , et al . FIFL: a fair incentive mechanism for federated learning [C ] // Proceedings of the 50th International Conference on Parallel Processing . New York : ACM Press , 2021 : 1 - 10 .

XU Y , LU Z , GAI K , et al . BESIFL: blockchain-empowered secure and incentive federated learning paradigm in IoT [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 8 ): 6561 - 6573 .

WEN Q , ZHOU T , ZHANG C , et al . Transformers in time series: a survey [C ] // Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence . 2023 : 6778 - 6786 .

FUNG C , YOON C J M , BESCHASTNIKH I . Mitigating sybils in federated learning poisoning [J ] . arXiv preprint arXiv: 1808.04866 , 2020 .

ZHOU S , LI K , XIAO L , et al . A systematic review of consensus mechanisms in blockchain [J ] . Mathematics , 2023 , 11 ( 10 ): 2248 .

PASS R , SHI E . Hybrid consensus: efficient consensus in the permissionless model [C ] // Proceedings of the International Symposium on Distributed Computing (DISC 2017). 2017 : 91 .

JAKOBSSON M , JUELS A . Proofs of work and bread pudding protocols [C ] // Proceedings of the IFIP TC6/TC11 Joint Working Conference on Secure Information Networks: Communications and Multimedia Security . 1999 : 258 - 272 .

KING S , NADAL S . PPCoin: peer-to-peer crypto-currency with proof-of-stake [C ] // King2012PPCoinPC . 2012 .

GRAMOLI V . From blockchain consensus back to Byzantine consensu [J ] . Future Generation Computer Systems , 2020 , 107 : 760 - 769 .

LI Y , CHEN C , LIU N , et al . A blockchain-based decentralized federated learning framework with committee consensus [J ] . IEEE Network , 2021 , 35 ( 1 ): 234 - 241 .

QU Y , POKHREL S R , GARG S , et al . A blockchained federated learning framework for cognitive computing in industry 4.0 networks [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 4 ): 2964 - 2973 .

BOUALOUACHE A , BRIK B , SENOUCI S M , et al . On-demand security framework for 5GB vehicular networks [J ] . IEEE Internet of Things Magazine , 2023 , 6 ( 2 ): 26 - 31 .

米波 , 翁渊 , 黄大荣 , 等 . 基于区块链共识激励机制的新型联邦学习系统 [J ] . 信息安全学报 , 2024 , 9 ( 1 ): 15 - 32 .

MI B , WENG Y , HUANG D R , et al . A novel FL system based on consensus motivated blockchain [J ] . Journal of Cyber Security , 2024 , 9 ( 1 ): 15 - 32 .

YING C , XIA F , WEI D S L , et al . BIT-FL: blockchain-enabled incentivized and secure federated learning framework [J ] . IEEE Transactions on Mobile Computing , 2025 , 24 ( 2 ): 1212 - 1229 .

BUTERIN V . A next-generation smart contract and decentralized application platform [C ] // Buterin2015ANG . 2015 .

WANG S . BlockFedML: blockchained federated machine learning systems [C ] // Proceedings of the International Conference on Intelligent Computing, Automation and Systems (ICICAS) . 2019 : 751 - 756 .

JABERZADEH A , SHRESTHA A K , KHAN F A , et al . Blockchain-based federated learning: incentivizing data sharing and penalizing dishonest behavior [J ] . arXiv preprint arXiv: 2307.10492 , 2023 .

XU G , KONG D , ZHANG K , et al . A model value transfer incentive mechanism for federated learning with smart contracts in aiot [J ] . IEEE Internet of Things Journal , 2024 , 12 ( 3 ): 2530 - 2544 .

LIU Z , CHEN Y , YU H , et al . GTG-Shapley: efficient and accurate participant contribution evaluation in federated learning [J ] . ACM Transactions on Intelligent Systems and Technology , 2022 , 13 ( 4 ): 1 - 21 .

SHI Z , ZHANG L , YAO Z , et al . FedFAIM: a model performance-based fair incentive mechanism for federated learning [J ] . IEEE Transactions on Big Data , 2022 , 1 - 13 .

SALARBASHISHAHRI M , OKEGBILE S D , CAI J . A shapley value-enhanced evaluation technique for effective aggregation in federated learning [C ] // Proceedings of the 2022 IEEE Future Networks World Forum (FNWF) . Piscataway : IEEE Press , 2022 : 88 - 93 .

RICHARDSON A , FILOS-RATSIKAS A , FALTINGS B . Budget-bounded incentives for federated learning [C ] // Proceedings of the Federated Learning . Berlin : Springer , 2020 : 176 - 188 .

COOK R D , WEISBERG S J T . Characterizations of an empirical influence function for detecting influential cases in regression [J ] . Technometrics , 1980 , 22 : 495 - 508 .

HUANG G , CHEN X , OUYANG T , et al . Collaboration in participant-centric federated learning: a game-theoretical perspective [J ] . IEEE Transactions on Mobile Computing , 2022 , 22 ( 11 ): 6311 - 6326 .

LI L , YU X , CAI X , et al . Contract-theory-based incentive mechanism for federated learning in health crowdsensing [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 5 ): 4475 - 4489 .

WANG B , LI H , LIU X , et al . FRAD: free-rider attacks detection mechanism for federated learning in AIoT [J ] . IEEE Internet of Things Journal , 2023 , 11 ( 3 ): 4377 - 4388 .

CHEN Y , ZHANG J , YEO C . Privacy-preserving knowledge transfer for intrusion detection with federated deep autoencoding gaussian mixture model [J ] . Information Sciences , 2022 , 609 : 1204 - 1220 .

ZHANG C , XU Y , ELAHI H , et al . A blockchain-based model migration approach for secure and sustainable federated learning in IoT systems [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 8 ): 6574 - 6585 .

CHEN Z , ZHANG H , LI X , et al . FDFL: fair and discrepancy-aware incentive mechanism for federated learning [J ] . IEEE Transactions on Information Forensics and Security , 2024 , 19 : 8140 - 8154 .

ZHENG J , LI G , WANG W , et al . Long-term incentive mechanism for federated learning: a dynamic repeated game approach [C ] // Proceedings of the 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) . Piscataway : IEEE Press , 2023 : 1 - 6 .

YE M , FANG X , DU B , et al . Heterogeneous federated learning: state-of-the-art and research challenges [J ] . ACM Computing Surveys , 2023 , 56 ( 3 ): 1 - 44 .

ZHAO P , LAI L . Minimax optimal Q learning with nearest neighbors [J ] . IEEE Transactions on Information Theory , 2024 , 71 ( 2 ): 1300 - 1322 .

SMITH V , CHIANG C K , SANJABI M , et al . Federated multi-task learning [C ] // Proceedings of the International Conference on Neural Information Processing Systems . 2017 : 4427 - 4437 .

MARFOQ O , NEGLIA G , BELLET A , et al . Federated multi-task learning under a mixture of distributions [J ] . Advances in Neural Information Processing Systems , 2021 , 34 : 15434 - 15447 .

HE C , CEYANI E , BALASUBRAMANIAN K , et al . Spreadgnn: decentralized multi-task federated learning for graph neural networks on molecular data [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . 2022 : 6865 - 6873 .

WU Z , SUN S , WANG Y , et al . FedICT: federated multi-task distillation for multi-access edge computing [J ] . IEEE Transactions on Parallel and Distributed Systems , 2024 , 35 ( 6 ): 952 - 966 .

CHEN Y Q , ZHANG T , JIANG X L , et al . FedBone: towards large-scale federated multi-task learning [J ] . Journal of Computer Science and Technology , 2024 , 39 ( 5 ): 1040 - 1057 .

ZHAO N , PEI Y , LIANG Y C , et al . Multi-agent deep reinforcement learning based incentive mechanism for multi-task federated edge learning [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 : 13530 - 13535 .

XUAN S , WANG M , ZHANG J , et al . An incentive mechanism design for federated learning with multiple task publishers by contract theory approach [J ] . Information Sciences , 2024 , 664 : 120330 .

XIE C , HUANG K , CHEN P Y , et al . DBA: distributed backdoor attacks against federated learning [C ] // Proceedings of the 8th International Conference on Learning Representations(ICLR) . 2020 : 1 - 19 .

LIU T , ZHANG Y , FENG Z , et al . Beyond traditional threats: a persistent backdoor attack on federated learning [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Hong Kong : AAAI Press , 2024 : 21359 - 21367 .

KAHLA M , CHEN S , JUST H A , et al . Label-only model inversion attacks via boundary repulsion [C ] // Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2022 : 15025 - 15033 .

SANYAL S , ADDEPALLI S , BABU R V . Towards data-free model stealing in a hard label setting [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2022 : 15284 - 15293 .

LI Z , ZHANG Y . Membership leakage in label-only exposures [C ] // Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security . New York : ACM Press , 2021 : 880 - 895 .

CHOQUETTE-CHOO C A , TRAMER F , CARLINI N , et al . Label-only membership inference attacks [C ] // Proceedings of the 38th International Conference on Machine Learning . New York : PMLR , 2021 : 1964 - 1974 .

ZHU T , YE D , ZHOU S , et al . Label-only model inversion attacks: attack with the least information [J ] . IEEE Transactions on Information Forensics and Security , 2023 , 18 : 991 - 1005 .

VO V , ABBASNEJAD E , RANASINGHE D C J A . RamBoAttack: a robust query efficient deep neural network decision exploit [J ] . arXiv preprint arXiv: 2112.05282 , 2021 .

WEI J , BAO E , XIAO X , et al . DPIS: an enhanced mechanism for differentially private SGD with importance sampling [C ] // Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security . New York : ACM Press , 2022 : 2885 - 2899 .

YE D , ZHU T , GAO K , et al . Defending against label-only attacks via meta-reinforcement learning [J ] . IEEE Transactions on Information Forensics and Security , 2024 , 19 : 3295 - 3308 .

BOUZINIS P S , DIAMANTOULAKIS P D , KARAGIANNIDIS G K . Incentive-based delay minimization for 6G-enabled wireless federated learning [C ] // Proceedings of the Frontiers in Communications and Networks . 2022 .

ZHU Y , LIU Z , WANG P , et al . A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G [J ] . Digital Communications and Networks , 2022 , 9 : 817 - 826 .

CAI L , DAI Y , HU Q , et al . Bayesian game-driven incentive mechanism for blockchain-enabled secure federated learning in 6G wireless networks [J ] . IEEE Transactions on Network Science and Engineering , 2024 , 11 ( 5 ): 4951 - 4964 .

THAPA C , ARACHCHIGE P C M , CAMTEPE S , et al . SplitFed: when federated learning meets split learning [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . AAAI Press , 2022 : 8485 - 8493 .

VEPAKOMMA P , SWEDISH T , RASKAR R , et al . No peek: a survey of private distributed deep learning [J ] . arXiv preprint arXiv: 1812.03288 , 2018 .

HAFI H , BRIK B , FRANGOUDIS P A , et al . Split federated learning for 6G enabled-networks: requirements, challenges, and future directions [J ] . IEEE Access , 2024 , 12 : 9890 - 9930 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

DP-FedDRC：缓解维度坍塌的差分隐私个性化联邦学习

FLWD:基于联邦学习的Webshell检测方法

基于生成对抗网络的隐私增强联邦学习方案

动态聚合权重的隐私保护联邦学习框架

基于差分隐私保护技术的多方求和查询方法