Computer Science ›› 2022, Vol. 49 ›› Issue (3): 23-30.doi: 10.11896/jsjkx.210800051

• Novel Distributed Computing Technology and System • Previous Articles     Next Articles

Incentive Mechanism for Hierarchical Federated Learning Based on Online Double Auction

DU Hui1,2, LI Zhuo1,2, CHEN Xin2   

  1. 1 Beijing Key Laboratory of Internet Culture and Digital Dissemination Research (Beijing Information Science & Technology University),Beijing 100101,China
    2 School of Computer Science,Beijing Information Science & Technology University,Beijing 100101,China
  • Received:2021-08-05 Revised:2021-10-19 Online:2022-03-15 Published:2022-03-15
  • About author:DU Hui,born in 1998,postgraduate.His main research interests include edge computing and so on.
    LI Zhuo,born in 1983,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include mobile wireless network and distributed computing.
  • Supported by:
    National Natural Science Foundation of China(61872044),Beijing Municipal Program for Top Talent and Open Program of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research.

Abstract: In hierarchical federated learning,energy constrained mobile devices will consume their own resources for participating in model training.In order to reduce the energy consumption of mobile devices,this paper proposes the problem of minimizing the sum of energy consumption of mobile devices without exceeding the maximum tolerance time of hierarchical federated learning.Different training rounds of edge server can select different mobile devices,and mobile devices can also train models under diffe-rent edge servers concurrently.Therefore,this paper proposes ODAM-DS algorithm based on an online double auction mechanism.Based on the optimal stopping theory,the edge server is supported to select the mobile device at the best time,so as to minimize the average energy consumption of the mobile device.Then,the theoretical analysis of the proposed online double auction mechanism proves that it meets the characteristics of incentive compatibility,individual rationality and weak budget equilibrium constraints.Simulation results show that the energy consumption of ODAM-DS algorithm is 19.04% lower than that of the existing HFEL algorithm.

Key words: Hierarchical federated learning, Incentive mechanism design, Minimization of energy consumption, Online double auction, Optimal stopping theory

CLC Number: 

  • TP393
[1]ZHOU Z,CHEN X,LI E,et al.Edge intelligence:paving the last mile of artificial intelligence with edge computing[J].Procee-dings of the IEEE,2019,107(8):1738-1762.
[2]MCMAHAN H B,MOORE E,RAMAGE D,et al.Communication-efficient learning of deep networks from decentralized data[J].arXiv:1602.05629v2,2016.
[3]LIM W Y B,LUONG N C,HOANG D H,et al.Federated lear-ning in mobile edge networks:A comprehensive survey[J].IEEE Communications Surveys & Tutorials,2020,22(3):2031-2063.
[4]BONAWITZ K,EICHNER H,GRIESKAM P,et al.Towardsfederated learning at scale:System design[J].arXiv:1902.01046,2019.
[5]WANG S Q,TOUR T,SALONIDIS T et al.Adaptive federated learning in resource constrained edge computing systems[J].IEEE Journal on Selected Areas in Communications (JSAC),2019,37(6):1205-1221.
[6]LIU L,ZHANG J,SONG S H,et al.Client-edge-cloud hierar-chical federated learning[J].arXiv:1095.06641v1,2019.
[7]LUO S,CHEN X,WU Q,et al.HFEL:Joint edge associationand resource allocation for cost-efficient hierarchical federated edge learning[J].arXiv:2002.11343,2020.
[8]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.
[9]CHAI H,LENG S,CHEN Y,et al.A Hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles[J].IEEE Transactions on Intelligent Transportation Systems,2020,22(7):3975-3986.
[10]ZHAN Y,ZHANG J.An incentive mechanism design for efficient edge learning by deep reinforcement learning approach[C]//IEEE INFOCOM:2020 IEEE Conference on Computer Communications.Toronto,ON,Canada:IEEE,2020:1-10.
[11]TANG J.Nodes incentive strategy based on bayesian game in Ad Hoc networks[J].Computer Engineering,2019,45(8):152-158,164.
[12]ZHOU Q,LI P,NIE L.User incentive mechanism based on spatial-temporal correlation for crowd sensing[J].Computer Engineering,2021,47(3):227-236.
[13]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 2020).Singapore,IEEE,2020:278-288.
[14]ZHANG X,YANG Z,ZHOU Z,et al.Free market of crowd-sourcing:incentive mechanism design for mobile sensing[J].IEEE Transactions on Parallel and Distributed Systems,2014,25(12):3190-3200.
[15]LI D,YANG Q,AN D,et al.On location privacy-preserving online double auction for electric vehicles in microgrids[J].IEEE Internet of Things Journal,2019,6(4):5902-5915.
[16]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.
[17]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]//WCNC:2020 IEEE Wireless Communications and Networking Conference.Seoul,Korea (South):IEEE,2020:1-6.
[18]PENG Y,WANG G C,HUANG S Q,et al.An energy consump-tion optimization strategy for data transmission based on optimal stopping theory in mobile network[J].Chinese Journal of Computers,2016,39(6):1162-1175.
[19]WANG F S,WANG G C.Study on energy minimization data transmission strategy in mobile cloud computing[C]//SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI:2018 IEEE SmartWorld,Ubiquitous Intelligence & Computing,Advanced &Trusted Computing,Scalable Computing & Communications,Cloud & Big Data Computing,Internet of People and Smart City Innovation.Guangzhou,China:IEEE,2018:1211-1218.
[1] HUANG Rong-xi, WANG Nao, XIE Tian-xiao, WANG Gao-cai. Study on Channel-aware Expected Energy Consumption Minimization Strategy in Wireless Networks [J]. Computer Science, 2018, 45(10): 130-137.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!