Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (3): 20-29.doi: 10.11896/jsjkx.230700194

• Information Security Protection in New Computing Mode • Previous Articles     Next Articles

Survey of Incentive Mechanism for Cross-silo Federated Learning

WANG Xin1,2, HUANG Weikou1, SUN Lingyun2   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 College of Computer Science and Technology,Zhejiang University,Hangzhou 310058,China
  • Received:2023-07-25 Revised:2023-12-10 Online:2024-03-15 Published:2024-03-13
  • About author:WANG Xin,born in 1984,Ph.D,asso-ciate professor,master supervisor,is a member of CCF(No.11687M).His mainresearch interests include machine lear-ning,big data analysis and federated learning.
  • Supported by:
    National Key R&D Program of China(2020YFB0906000,2020YFB0906004) and Zhejiang University of Technology Science and Technology Project(KYY-HX-20220288,KYY-HX-20180649).

PDF (PC)

1080

Abstract: As a kind of distributed machine learning,federated learning effectively solves the problem of data sharing in big data era.Among them,cross-silo federated learning,as a type of federated learning in which institutions cooperate with each other,is obviously very important to design a reasonable incentive mechanism in the process of cross-silo cooperation.Based on the perspective of cross-silo cooperation,this paper makes a comprehensive analysis of the existing incentive mechanism of cross-silo fe-derated learning.Firstly,this paper introduces three basic problems in the process of cross-silo cooperation:high privacy,data he-terogeneity,and fairness.Then,it analyzes the incentive mechanism design methods under two different cross-silo cooperation models centered on the global model and centered on participants.Finally,it summarizes the several factors that affect the stable development of cross-silo cooperation:data evolution of participants,changes in the cooperative relationship of participants,and negative behaviors of participants,and looks forward to the future direction of cross-silo federal cooperation.

Key words: Cross-silo federated learning, Incentive mechanism, Cross-silo cooperation, Distributed machine learning, Privacy computing

CLC Number: 

  • TP181
[1]MCMAHAN B,MOORE E,RAMAGE D,et al.Communica-tion-efficient learning of deep networks from decentralized data [C]//Artificial Intelligence and Statistics.PMLR,2017:1273-1282.
[2]NIE W,XIN L,LI Z.Study on the Application of FederatedLearning in Medical Informatization[J]. Journal of Medical Informatics,2022,43(10):12-13.
[3]ZHAN Y,ZHANG J,HONG Z,et al.A survey of incentivemechanism design for federated learning[J].IEEE Transactions on Emerging Topics in Computing,2021,10(2):1035-1044.
[4]ZENG R,ZENG C,WANG X,et al.Incentive Mechanisms in Federated Learning and A Game-Theoretical Approach [J].IEEE Network,2022,36(6):229-235.
[5]LIANG W,LIU B,LIN W,et al.Survey of Incentive Mechanism for Federated Learning [J].Computer Science,2022,49(12):46-52.
[6]TU X,ZHU K,LUONG N,et al.Incentive mechanisms for fe-derated learning:From economic and game theoretic perspective [J].IEEE Transactions on Cognitive Communications and Networking,2022,8(3):1566-1593.
[7]WITT L,HEYER M,TOYODA K,et al.Decentral and In-centivized Federated Learning Frameworks:A Systematic Literature Review [J].IEEE Internet of Things Journal,2023,10(4):3642-3663.
[8]LIU Y,KANG Y,ZOU T,et al.Vertical federated learning [J].arXiv:2211.12814,2022.
[9]YAN Y,LIGETI P.A Survey of Personalized and IncentiveMechanisms for Federated Learning [C]//2022 IEEE 2nd Conference on Information Technology and Data Science.IEEE,2022:324-329.
[10]HUANG C,HUANG J,LIU X.Cross-silo federated learning:Challenges and opportunities [J].arXiv:2206.12949,2022.
[11]DWORK C.Differential privacy [C]//International Colloquium on Automata,Languages,and Programming.Berlin,Heidelberg:Springer,2006:1-12.
[12]WANG C,WU X,LIU G,et al.Safeguarding cross-silo federated learning with local differential privacy [J].Digital Communications and Networks,2022,8(4):446-454.
[13]REN K.Differentially Private Auction for Federated Learning with Non-IID Data [C]//2022 International Conference on Service Science.IEEE,2022:305-312.
[14]ZHANG L,ZHU T,XIONG P,et al.A Game-theoretic Federated Learning Framework for Data Quality Improvement [J].IEEE Transactions on Knowledge and Data Engineering,2023,35(11):10952-10966.
[15]WANG D,REN J,WANG Z,et al.PrivAim:A Dual-PrivacyPreserving and Quality-Aware Incentive Mechanism for Federated Learning [J].IEEE Transactions on Computers,2023,72(7):1913-1927.
[16]WEI K,LI J,DING M,et al.Federated learning with differential privacy:Algorithms and performance analysis[J].IEEE Trans-actions on Information Forensics and Security,2020(15):3454-3469.
[17]LIU K,HU S,WU S Z,et al.On privacy and personalization in cross-silo federated learning [J].Advances in Neural Information Processing Systems,2022,35:5925-5940.
[18]ZHENG S,CAO Y,YOSHIKAWA M.Secure shapley value for cross-silo federated learning [J].arXiv:2209.04856,2022.
[19]LUO F,AL-KUWARI S,DING Y.SVFL:Efficient Secure Aggregation and Verification for Cross-Silo Federated Learning [J].IEEE Transactions on Mobile Computing,2022(1):1-14.
[20]TRAN H Y,HU J,YIN X,et al.An Efficient Privacy-Enhan-cing Cross-Silo Federated Learning and Applications for False Data Injection Attack Detection in Smart Grids [J].IEEE Transactions on Information Forensics and Security,2023,18:2538-2552.
[21]LI Q,WEN Z,WU Z,et al.A Survey on Federated Learning Systems:Vision,Hype and Reality for Data Privacy and Protection [J].IEEE Transactions on Knowledge and Data Engineering,2023,35(4):3347-3366.
[22]ZHANG W,ZHOU T,LU Q,et al.Dynamic-fusion-based fede-rated learning for COVID-19 detection [J].IEEE Internet of Things Journal,2021,8(21):15884-15891.
[23]LI L,YU X,CAI X,et al.Contract-Theory-Based IncentiveMechanism for Federated Learning in Health CrowdSensing [J].IEEE Internet of Things Journal,2022,10(5):4475-4489.
[24]LI B,SHI Y,GUO Y,et al.Incentive and Knowledge Distillation Based Federated Learning for Cross-Silo Applications [C]//IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops.IEEE,2022:1-6.
[25]TRAN V T,PHAM H H,WONG K S.Personalized privacy-preserving framework for cross-silo federated learning [J].ar-Xiv:2302.12020,2023.
[26]LUO J,WU S.Adapt to Adaptation:Learning Personalization for Cross-Silo Federated Learning [C]//IJCAI:Proceedings of the Conference.2022.
[27]YANG X,TAN W,PENG C,et al.Federated learning incentive mechanism design via enhanced Shapley value method [J].Wireless Communications and Mobile Computing,2022,22:1-11.
[28]HUANG C,KE S,KAMHOUA C,et al.Incentivizing data contribution in cross-silo federated learning[J].arXiv:2203.03885,2022.
[29]GU T,LI L,CHANG L,et al.Fair federated machine learning and its design:A comprehensive survey [J].Chinese Journal of Conputers,2020,46(9):1991-2024.
[30]TRAVADI Y,PENG L,BI X,et al.Welfare and Fairness Dynamics in Federated Learning:A Client Selection Perspective [J].arXiv:2302.08976,2023.
[31]SHI Z,ZHANG L,YAO Z,et al.FedFAIM:A Model Perfor-mance-based Fair Incentive Mechanism for Federated Learning [J].IEEE Transactions on Big Data,2022(1):1-13.
[32]CHU L,WANG L,DONG Y,et al.Fedfair:Training fair models in cross-silo federated learning [J].arXiv:2109.05662,2021.
[33]ZHANG X,DOU Y,ZHANG C,et al.Reserch on the Profit-sharing Mechanism for Federated Learning [J].Frontiers of Science and Technology of Engineering Management,2023,42(2):8-15.
[34]HAN J,KHAN A F,ZAWAD S,et al.Tiff:Tokenized incentive for federated learning [C]//2022 IEEE 15th International Conference on Cloud Computing.IEEE,2022:407-416.
[35]CAO X,LI Z,SUN G,et al.Cross-Silo Heterogeneous Model Federated Multitask Learning [J].arXiv:2202.08603,2022.
[36]JIANG S,WU J.Coalition Formation Game in the Cross-SiloFederated Learning System [C]//2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems.IEEE,2022:49-57.
[37]FRABONI Y,VIDAL R,LORENZI M.Free-rider attacks onmodel aggregation in federated learning [C]//International Conference on Artificial Intelligence and Statistics.PMLR,2021:1846-1854.
[38]RANATHUNGA T,MCGIBNEY A,REA S,et al.Blockchain-Based Decentralized Model Aggregation for Cross-Silo Federated Learning in Industry 4.0 [J].IEEE Internet of Things Journal,2022,10(5):4449-4461.
[39]WANG Y,LIG,LI K.Survey on Contribution Evaluation for Federated Learning [J].Journal of Software,2023,34(3):1168-1192.
[40]YANG C,LIU J,SUN H,et al.WTDP-Shapley:Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection [J].IEEE Transactions on Big Data,2022(1):1-10.
[41]WANG Z,YAN B,DONG A.Blockchain Empowered Federated Learning for Data Sharing Incentive Mechanism [J].Procedia Computer Science,2022,202:348-353.
[42]TANG M,WONG V.An incentive mechanism for cross-silo fe-derated learning:A public goods perspective [C]//IEEE INFOCOM 2021-IEEE Conference on Computer Communications.IEEE,2021:1-10.
[43]YANG Y,ZHOU Y,HU M,et al.BARA:Efficient IncentiveMechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning[J].arXiv:2305.05221,2023.
[44]LIU Y,TIAN M,CHEN Y,et al.A contract theory based incentive mechanism for federated learning [M]//Federated and Transfer Learning.Cham:Springer International Publishing,2022:117-137.
[45]LUO X,ZHANG Z,HE J,et al.Strategic Analysis of the Pa-rameter Servers and Participants in Federated Learning:An Evolutionary Game Perspective [J].IEEE Transactions on Computational Social Systems,2024,11(1):132-143.
[46]CHEN Y,ZHANG Y,WANG S,et al.Dim-ds:Dynamic incentive model for data sharing in federated learning based on smart contracts and evolutionary game theory [J].IEEE Internet of Things Journal,2022,9(23):24572-24584.
[47]WANG W.Research on Incentive Mechanism of FederatedLearning Based on Repeated Game [D].Xi'an:Xidian University,2022.
[48]LI Y,WANG X,ZENG R,et al.VARF:An Incentive Mechanism of Cross-silo Federated Learning in MEC [J].IEEE Internet of Things Journal,2023,10(17):15115-15132.
[49]YUAN S,LIU H,LV H,et al.Adaptive incentive for cross-silo federated learning:A multi-agent reinforcement learning approach [J].arXiv:2302.07493,2023.
[50]LU J,PAN B,SEID A,et al.Truthful incentive mechanism design via internalizing externalities and lp relaxation for vertical federated learning [J].IEEE Transactions on Computational Social Systems,2023,10(6):2909-2923.
[51]XIONG A,CHEN Y,CHEN H,et al.A Truthful and Reliable Incentive Mechanism for Federated Learning Based on Reputation Mechanism and Reverse Auction [J].Electronics,2023,12(3):517-540.
[52]XU H,NANDA P,LIANG J,et al.The Force of Compensation,a Multi-stage Incentive Mechanism Model for Federated Lear-ning[C]//International Conference on Network and System Security.Cham:Springer Nature Switzerland,2022:357-373.
[53]LIN J,DU M,LIU J.Free-riders in federated learning:Attacks and defenses [J].arXiv:1911.12560,2019.
[54]HUANG J,TALBI R,ZHAO Z,et al.An exploratory analysis on users' contributions in federated learning [C]//2020 Second IEEE International Conference on Trust,Privacy and Security in Intelligent Systems and Applications.IEEE,2020:20-29.
[55]ZHANG N,MA Q,CHEN X.Enabling Long-Term Cooperation in Cross-Silo Federated Learning:A Repeated Game Perspective [J].IEEE Transactions on Mobile Computing,2023,22(7):3910-3924.
[56]CHEN J,HU Q,JIANG H.Social welfare maximization incross-silo federated learning [C]//2022 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP 2022).IEEE,2022:4258-4262.
[57]QI J,LIN F,CHEN Z,et al.High-quality model aggregation for blockchain-based federated learning via reputation-motivated task participation [J].IEEE Internet of Things Journal,2022,9(19):18378-18391.
[58]PANDL K,LEISER F,THIEBES S,et al.Reward Systems for Trustworthy Medical Federated Learning [J].arXiv:2205.00470,2022.
[1] LI Ke, YANG Ling, ZHAO Yanbo, CHEN Yonglong, LUO Shouxi. EGCN-CeDML:A Distributed Machine Learning Framework for Vehicle Driving Behavior Prediction [J]. Computer Science, 2023, 50(9): 318-330.
[2] FENG Chen, GU Jingjing. Efficient Distributed Training Framework for Federated Learning [J]. Computer Science, 2023, 50(11): 317-326.
[3] XU Miaomiao, CHEN Zhenping. Incentive Mechanism for Continuous Crowd Sensing Based Symmetric Encryption and Double Truth Discovery [J]. Computer Science, 2023, 50(1): 294-301.
[4] FU Yan-ming, ZHU Jie-fu, JIANG Kan, HUANG Bao-hua, MENG Qing-wen, ZHOU Xing. Incentive Mechanism Based on Multi-constrained Worker Selection in Mobile Crowdsourcing [J]. Computer Science, 2022, 49(9): 275-282.
[5] WANG Si-ming, TAN Bei-hai, YU Rong. Blockchain Sharding and Incentive Mechanism for 6G Dependable Intelligence [J]. Computer Science, 2022, 49(6): 32-38.
[6] YANG Xin-yu, PENG Chang-gen, YANG Hui, DING Hong-fa. Rational PBFT Consensus Algorithm with Evolutionary Game [J]. Computer Science, 2022, 49(3): 360-370.
[7] DU Hui, LI Zhuo, CHEN Xin. Incentive Mechanism for Hierarchical Federated Learning Based on Online Double Auction [J]. Computer Science, 2022, 49(3): 23-30.
[8] WANG Xin, ZHOU Ze-bao, YU Yun, CHEN Yu-xu, REN Hao-wen, JIANG Yi-bo, SUN Ling-yun. Reliable Incentive Mechanism for Federated Learning of Electric Metering Data [J]. Computer Science, 2022, 49(3): 31-38.
[9] LIANG Wen-ya, LIU Bo, LIN Wei-wei, YAN Yuan-chao. Survey of Incentive Mechanism for Federated Learning [J]. Computer Science, 2022, 49(12): 46-52.
[10] WANG Qin, WEI Li-fei, LIU Ji-hai, ZHANG Lei. Private Set Intersection Protocols Among Multi-party with Cloud Server Aided [J]. Computer Science, 2021, 48(10): 301-307.
[11] CHEN Meng-rong,LIN Ying,LAN Wei,SHAN Jin-zhao. Improvement of DPoS Consensus Mechanism Based on Positive Incentive [J]. Computer Science, 2020, 47(2): 269-275.
[12] SHU Na,LIU Bo,LIN Wei-wei,LI Peng-fei. Survey of Distributed Machine Learning Platforms and Algorithms [J]. Computer Science, 2019, 46(3): 9-18.
[13] TONG Hai,BAI Guang-wei,SHEN Hang. Double-auction-based Incentive Mechanism for k-anonymity [J]. Computer Science, 2019, 46(3): 202-208.
[14] LIAO Xin-kao and WANG Li-sheng. Research on Incentive Mechanism Based on Social Norms and Boycott [J]. Computer Science, 2014, 41(4): 28-30.
[15] . Trust Management Model Based on Evaluation of Resources [J]. Computer Science, 2012, 39(8): 31-.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.