A4 Article in conference proceedings
Incentive Mechanism Design For Federated Learning in Multi-access Edge Computing (2022)


Liu, J., Chang, Z., Min, G., & Han, Z. (2022). Incentive Mechanism Design For Federated Learning in Multi-access Edge Computing. In GLOBECOM 2022 IEEE Global Communications Conference (pp. 3454-3459). IEEE. https://doi.org/10.1109/GLOBECOM48099.2022.10000933


JYU authors or editors


Publication details

All authors or editorsLiu, Jingyuan; Chang, Zheng; Min, Geyong; Han, Zhu

Parent publicationGLOBECOM 2022 IEEE Global Communications Conference

Place and date of conferenceRio de Janeiro, Brazil4.-8.12.2022

ISBN978-1-6654-3541-3

eISBN978-1-6654-3540-6

Publication year2022

Publication date11/01/2023

Pages range3454-3459

PublisherIEEE

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/GLOBECOM48099.2022.10000933

Publication open accessNot open

Publication channel open access


Abstract

Federated learning (FL) is a type of distributed machine learning in which mobile users can train data locally and send the results to the FL server to update the global model. However, the implementation of FL may be prevented by the self-fish nature of mobile users, as they need to contribute considerable data and computing resources for participating in the FL process. Therefore, it is of importance to design the incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission, we design an incentive scheme to facilitate the FL process by investigating interactions between the multi-access edge computing (MEC) server and mobile users in a MEC-based FL system. By using a two-stage Stackelberg game model, we explore the transmission power allocation of the users and reward policy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results show that our model is effective for different parameter settings and the utility of the MEC server can be increased significantly compared to the baseline.


Keywordsmachine learningedge computingmobile deviceswireless data transmissionresource allocationgame theory

Free keywordsFederated learning; multi-access edge computing; incentive mechanism; power allocation


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-25-02 at 19:46