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
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