Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (12): 46-52.doi: 10.11896/jsjkx.220500272

• Federated Leaming • Previous Articles     Next Articles

Survey of Incentive Mechanism for Federated Learning

LIANG Wen-ya1, LIU Bo1, LIN Wei-wei2,3, YAN Yuan-chao1   

  1. 1 School of Computer Science,South China Normal University,Guangzhou 510631,China
    2 School of Computer Science and Engineering,South China University of Technology,Guangzhou 510640,China
    3 Pengcheng Laboratory,Shenzhen,Guangdong 518066,China
  • Received:2022-05-29 Revised:2022-08-05 Online:2022-12-15 Published:2022-12-14
  • About author:LIANG Wen-ya,born in 1997,postgra-duate.Her main research interests include federated learning and incentive mechanism.LIU Bo,born in 1968,Ph.D,professor,is a member of China Computer Federation.His main research interests include cloud computing,big data technology and distributed security technology.LIN Wei-wei,born in 1980,Ph.D,professor,is a member of China Computer Federation.His main research interests include cloud computing,big data technology and AI application technology.
  • Supported by:
    Key Research and Development Program of Guangdong Province(2021B0101420002),General Project of National Natural Science Foundation of China(62002078,61872084),Guangzhou Development Zone International Cooperation Project(2020GH10) and Major Key Project of PCL(PCL2021A09).

PDF (PC)

872

Abstract: Federated Learning(FL) is driven by multi-party data participation,where participants and central servers continuously exchange model parameters rather than directly upload raw data to achieve data sharing and privacy protection.In practical applications,the accuracy of the FL global model relies on multiple stable and high-quality clients participating,but there is an imba-lance in the data quality of participating clients,which can lead to the client being in an unfair position in the training process or not participating in training.Therefore,how to motivate clients to participate in federated learning actively and reliably is the key,which ensuring that FL is widely promoted and applied.This paper mainly introduces the necessity of incentive mechanisms in FL and divides the existing research into incentive mechanisms based on contribution measurement,client selection,payment allocation and multiple sub-problems optimization according to the sub-problems of incentive mechanisms in the FL training process,analyzes and compares existing incentive schemes,and summarizes the challenges in the development of incentive mechanisms on this basis,and explores the future research direction of FL incentive mechanisms.

Key words: Federated learning, Incentive mechanism, Contribution measurement, Client selection, Payment allocation

CLC Number: 

  • TP181
[1]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444.
[2]LIM W Y B,LUONG N C,HOANG D T,et al.Federated lear-ning in mobile edge networks:A comprehensive survey[J].IEEE Communications Surveys & Tutorials,2020,22(3):2031-2063.
[3]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.
[4]LIM W Y B,NG J S,XIONG Z,et al.Decentralized edge intelligence:A dynamic resource allocation framework for hierarchical federated learning[J].IEEE Transactions on Parallel and Distributed Systems,2021,33(3):536-550.
[5]MEHMOOD A,NATGUNANATHAN I,XIANG Y,et al.Protection of big data privacy[J].IEEE Access,2016,4:1821-1834.
[6]LUNDERVOLD A S,LUNDERVOLD A.An overview of deep learning in medical imaging focusing on MRI[J].Zeitschrift fur Medizinische Physik,2019,29(2):102-127.
[7]ZHAO Y,ZHAO J,JIANG L,et al.Privacy-preserving blockchain-based federated learning for IoT devices[J].IEEE Internet of Things Journal,2020,8(3):1817-1829.
[8]AHMED L,AHMAD K,SAID N,et al.Active Learning Based Federated Learning for Waste and Natural Disaster Image Classification[J].IEEE Access,2020,8:208518-208531.
[9]LI T,SAHU A K,ZAHEER M,et al.Federated optimization in heterogeneous networks[J].Proceedings of Machine Learning and Systems,2020,2:429-450.
[10]LIU J,WANG J H,RONG C,et al.FedPA:An adaptively partial model aggregation strategy in Federated Learning[J].Computer Networks,2021,199:108468.
[11]BONAWITZ K,IVANOV V,KREUTER B,et al.Practical secure aggregation for privacy-preserving machine learning[C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security.2017:1175-1191.
[12]LI S B,YANG L,LI C J,et al.Overview of Federated Learning:Technology,Applications and Future[J].Computer Integrated Manufacturing System,2022,28(7):2119-2138.
[13]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,2022,10(2):1035-1044.
[14]ZENG R,ZENG C,WANG X,et al.A Comprehensive Survey of Incentive Mechanism for Federated Learning[J].arXiv:2106.15406,2021.
[15]BLUM A,HAGHTALAB N,PHILLIPS R L,et al.One for one,or all for all:Equilibria and optimality of collaboration in federated learning[C]//International Conference on Machine Learning.PMLR,2021:1005-1014.
[16]ZHANG X N,ZHU J M,GAO S,et al.Federal learning incentive mechanism based on blockchain and Bayesian game[J].Chinese Science:Information Science,2022,52(6):971-991.
[17]NG K L,CHEN Z,LIU Z,et al.A multi-player game for stu-dying federated learning incentive schemes[C]//Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence.2021:5279-5281.
[18]KANG J,XIONG Z,NIYATO D,et al.Incentive design for efficient federated learning in mobile networks:A contract theoryapproach[C]//2019 IEEE VTS Asia Pacific Wireless Communications Symposium(APWCS).IEEE,2019:1-5.
[19]ZHAN Y,LI P,QU Z,et al.A learning-based incentive mechanism for federated learning[J].IEEE Internet of Things Journal,2020,7(7):6360-6368.
[20]SONG T,TONG Y,WEI S.Profit allocation for federated lear-ning[C]//2019 IEEE International Conference on Big Data(Big Data).IEEE,2019:2577-2586.
[21]ZHANG J,LI C,ROBLES-KELLY A,et al.Hierarchically fair federated learning[J].arXiv:2004.10386,2020.
[22]NISHIO T,SHINKUMAR,MANDAYAM N B.Estimation of individual device contributions for incentivizing federated lear-ning[C]//2020 IEEE Globecom Workshops.IEEE,2020:1-6.
[23]YAN B,LIU B,WANG L,et al.Fedcm:A real-time contribution measurement method for participants in federated learning[C]//2021 International Joint Conference on Neural Networks(IJCNN).IEEE,2021:1-8.
[24]ZHANG J,WU Y,PAN R.Incentive mechanism for horizontal federated learning based on reputation and reverse auction[C]//Proceedings of the Web Conference 2021.2021:947-956.
[25]TANG M,WONG V W S.An incentive mechanism for cross-silo federated learning:A public goods perspective[C]//IEEE INFOCOM 2021-IEEE Conference on Computer Communications.IEEE,2021:1-10.
[26]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.
[27]HU R,GONG Y.Trading data for learning:Incentive mechanism for on-device federatedlearning[C]//GLOBECOM 2020－2020 IEEE Global Communications Conference.IEEE,2020:1-6.
[28]LE T H T,TRAN N H,TUN Y K,et al.An incentive mechanism for federated learning in wireless cellular networks:An auction approach[J].IEEE Transactions on Wireless Communications,2021,20(8):4874-4887.
[29]KHAN L U,PANDEY S R,TRAN N H,et al.Federated lear-ning for edge networks:Resource optimization and incentive mechanism[J].IEEE Communications Magazine,2020,58(10):88-93.
[30]PANDEY S R,TRAN N H,BENNIS M,et al.A crowdsourcing framework for on-device federated learning[J].IEEE Transactions on Wireless Communications,2020,19(5):3241-3256.
[31]CONG M,YU H,WENG X,et al.A vcg-based fair incentive mechanism for federated learning[J].arXiv:2008.06680,2020.
[32]LEE J,KIM D,NIYATO D.A Novel Joint Dataset and Incentive Management Mechanism for Federated Learning Over MEC[J].IEEE Access,2022,10:30026-30038.
[33]PANDEY S R,TRAN N H,BENNIS M,et al.Incentivize tobuild:A crowdsourcing framework for federated learning[C]//2019 IEEE Global Communications Conference(GLOBECOM).IEEE,2019:1-6.
[34]YU H,LIU Z,LIU Y,et al.A sustainable incentive scheme for federated learning[J].IEEE Intelligent Systems,2020,35(4):58-69.
[35]LIU Y,AI Z,SUN S,et al.Fedcoin:A peer-to-peer payment system for federated learning[M]//Federated Learning.Cham:Springer,2020:125-138.
[36]ZENG R,ZHANG S,WANG J,et al.Fmore:An incentivescheme of multi-dimensional auction for federated learning in mec[C]//2020 IEEE 40th International Conference on Distri-buted Computing Systems(ICDCS).IEEE,2020:278-288.
[37]ZHANG J,WU Y,PAN R.Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement[J].arXiv:2201.02410,2022.
[38]HAN J,KHAN A F,ZAWAD S,et al.Tokenized Incentive for Federated Learning[C]//Proceedings of the Federated Learning Workshop at the Association for the Advancement of Artificial Intelligence(AAAI) Conference.2022.
[39]WANG Z,HU Q,LI R,et al.Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Lear-ning[J].arXiv:2202.10938,2022.
[40]WANG G,DANG C X,ZHOU Z.Measure contribution of participants in federated learning[C]//2019 IEEE International Conference on Big Data(Big Data).IEEE,2019:2597-2604.
[41]ZHANG J,GUO S,MA X,et al.Parameterized KnowledgeTransfer for Personalized Federated Learning[J].Advances in Neural Information Processing Systems,2021,34:10092-10104.
[42]LE T H T,TRAN N H,TUN Y K,et al.Auction based incentive design for efficient federated learning in cellular wireless networks[C]//2020 IEEE Wireless Communications and Networking Conference(WCNC).IEEE,2020:1-6.
[43]GAO L,LI L,CHEN Y,et al.FGFL:A blockchain-based fair incentive governor for Federated Learning[J].Journal of Parallel and Distributed Computing,2022,163:283-299.
[44]TAHANIAN E,AMOUEI M,FATEH H,et al.A game-theoretic approach for robust federated learning[J].International Journal of Engineering,2021,34(4):832-842.
[1] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Federated Learning Based on Stratified Sampling Optimization for Heterogeneous Clients [J]. Computer Science, 2022, 49(9): 183-193.
[2] 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.
[3] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[4] CHEN Ming-xin, ZHANG Jun-bo, LI Tian-rui. Survey on Attacks and Defenses in Federated Learning [J]. Computer Science, 2022, 49(7): 310-323.
[5] LU Chen-yang, DENG Su, MA Wu-bin, WU Ya-hui, ZHOU Hao-hao. Clustered Federated Learning Methods Based on DBSCAN Clustering [J]. Computer Science, 2022, 49(6A): 232-237.
[6] YAN Meng, LIN Ying, NIE Zhi-shen, CAO Yi-fan, PI Huan, ZHANG Lan. Training Method to Improve Robustness of Federated Learning [J]. Computer Science, 2022, 49(6A): 496-501.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] ZHAO Luo-cheng, QU Zhi-hao, XIE Zai-peng. Study on Communication Optimization of Federated Learning in Multi-layer Wireless Edge Environment [J]. Computer Science, 2022, 49(3): 39-45.
[12] ZOU Sai-lan, LI Zhuo, CHEN Xin. Study on Transmission Optimization for Hierarchical Federated Learning [J]. Computer Science, 2022, 49(12): 5-16.
[13] SHEN Zhen, ZHAO Cheng-gui. Storage Task Allocation Algorithm in Decentralized Cloud Storage Network [J]. Computer Science, 2022, 49(12): 17-21.
[14] YANG Hong-jian, HU Xue-xian, LI Ke-jia, XU Yang, WEI Jiang-hong. Study on Privacy-preserving Nonlinear Federated Support Vector Machines [J]. Computer Science, 2022, 49(12): 22-32.
[15] QU Xiang-mou, WU Ying-bo, JIANG Xiao-ling. Federated Data Augmentation Algorithm for Non-independent and Identical Distributed Data [J]. Computer Science, 2022, 49(12): 33-39.
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.