Journal of Guangdong University of Technology ›› 2021, Vol. 38 ›› Issue (06): 47-52,83.doi: 10.12052/gdutxb.210114

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Incentivizing Resource Cooperation for Federated Learning in 6G Networks

Jiang Li1, Xie Sheng-li1, Zhang Yan2   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. Department of Informatics, University of Oslo, Oslo 0316, Norway
  • Received:2021-07-14 Online:2021-11-10 Published:2021-11-09

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Abstract: Recent advances in 6th Generation (6G) mobile networks can meet the needs of deeper intelligent communication. Meanwhile, it also brings great challenges to the security and privacy preservation of user data. Federated learning is emerging as a distributed learning method to preserve privacy by enabling users to train machine learning models locally and requiring the users to upload only model parameters instead of sending original data to the server. However, the users with mobile devices may be unwilling to participate in federated learning tasks due to considerable overhead for computation and communication, which degrades performance and hinders the application of federated learning. In this study, an iterative double auction is designed based on resource cooperation scheme to incentivize the mobile devices to contribute their resources in federated learning tasks, where model trainers act as sellers and task requesters as buyers. Access point determines optimal training time cost and payment according to the prices offered by the sellers and the buyers. The goal of our incentivizing resource cooperation scheme is to maximize the total utility of federated learning market under information asymmetry. Numerical results show that the proposed scheme can converge to the optimal solution, and also can significantly improve model accuracy and degrade model loss value.

Key words: 6G mobile networks, security and privacy preservation, federated learning, resource cooperation, iterative double auction

CLC Number: 

  • TN929.5
[1] 索士强, 王映民. 未来6G网络内生智能的探讨与分析[J]. 移动通信, 2020, 44(6): 126-30.
SUO S Q, WANG Y M. Discussion and analysis on the native intelligence for the future 6G network [J]. Mobile Communications, 2020, 44(6): 126-30.
[2] 孙耀华, 王则予, 袁硕, 等. 智能内生6G网络: 架构、用例和挑战[J]. 电子技术应用, 2021, 47(3): 8-13.
SUN Y H, WANG Z Y, YUAN S, et al. The sixth-generation mobile communication network with endogenous intelligence architectures, use cases and challenges [J]. Application of Electronic Technique, 2021, 47(3): 8-13.
[3] 郑思远, 崔苗, 张广驰. 基于强化学习的无人机安全通信轨迹在线优化策略[J]. 广东工业大学学报, 2021, 38(4): 60-64.
ZHENG S Y, CUI M, ZHANG G C. Reinforcement learning-based online trajectory optimization for secure UAV communications [J]. Journal of Guangdong University of Technology, 2021, 38(4): 60-64.
[4] 张成磊, 付玉龙, 李晖, 等. 6G网络安全场景分析及安全模型研究[J]. 网络与信息安全学报, 2021, 7(1): 29-45.
ZHANG C L, FU Y L, LI H, et al. Research on security scenarios and security models for 6G networking [J]. Chinese Journal of Network and Information Security, 2021, 7(1): 29-45.
[5] KONECNY J, MCMAHAN H B, RAMAGE D. Federated optimization: distributed machine learning for on-device intelligence[EB/OL]. [2016-10-08]. https://arxiv.org/abs/1610.02527v1.
[6] HARD A, RAO K, MATHEWS R, et al. Federated learning for mobile keyboard prediction [EB/OL]. [2019-02-28]. https://arxiv.org/abs/1811.03604v2.
[7] CHEN M, YANG Z, SAAD W, et al. A joint learning and communications framework for federated learning over wireless networks [J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 269-283.
[8] NI W, LIU Y, TIAN H. Intelligent reflecting surfaces enhanced federated learning[C]//IEEE Globecom Workshops. Taipei: IEEE, 2021: 7-11.
[9] LU Y, HUANG X, ZHANG K, et al. Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles [J]. IEEE Transactions on Vehicular Technology, 2020, 69(4): 4298-4311.
[10] LE T, TRAN N, TUN Y, et al. An incentive mechanism for federated learning in wireless cellular network: an auction approach [J]. IEEE Transactions on Wireless Communications, 2021, 20(8): 4874-4887.
[11] SUN W, WANG P, XU N, et al. Dynamic digital twin and distributed incentives for resource allocation in aerial-assisted internet of vehicles[J/OL]. IEEE Internet of Things Journal (Early Access) [2021-07-01]. https://ieeexplore.ieee.org/document/9351542. DOI: 10.1109/JIOT.2021.3058213.
[12] TRAN N, BAO W, ZOMAYA A, et al. Federated learning over wireless networks: optimization model design and analysis [C]//IEEE Infocom 2019-IEEE Conference on Computer Communications. Paris: IEEE, 2019: 1387-1395.
[13] FRIEDMAN D, RUST J. The double auction market: institutions theories and evidence [M]. Boulder: Westview Press, 1993: 429.
[14] BOYD S, VANDENBERGHE L. Convex optimization [M]. New York: Cambridge University Press, 2004: 727.
[15] KRIZHEVSKY A. Learning multiple layers of features from tiny images [D]. Toronto: University of Toronto, 2009.
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