A1 Journal article (refereed)
Game-Theoretic Power Allocation and Client Selection for Privacy-Preserving Federated Learning in IoMT (2025)


Liu, J., Chang, Z., Ye, C., Mumtaz, S., & Hämäläinen, T. (2025). Game-Theoretic Power Allocation and Client Selection for Privacy-Preserving Federated Learning in IoMT. IEEE Transactions on Communications, Early Access. https://doi.org/10.1109/tcomm.2024.3523968


JYU authors or editors


Publication details

All authors or editorsLiu, Jingyuan; Chang, Zheng; Ye, Chaoxiong; Mumtaz, Shahid; Hämäläinen, Timo

Journal or seriesIEEE Transactions on Communications

ISSN0090-6778

eISSN1558-0857

Publication year2025

VolumeEarly Access

PublisherInstitute of Electrical and Electronics Engineers (IEEE)

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/tcomm.2024.3523968

Publication open accessNot open

Publication channel open access


Abstract

In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. Federated learning (FL) can enhance the utilization of patient data while protecting privacy. Despite the great potential of FL to enhance the architecture of IoMT, the need for effective interference management and the limited energy resources of IoMT devices make the integration of FL into IoMT environments particularly challenging. This study proposes an innovative framework to address these challenges by optimizing power allocation and client selection across participating IoMT devices in the FL process. By employing a Stackelberg game model, our approach orchestrates power allocation among IoMT devices to enhance communication efficiency while adhering to strict differential privacy (DP) standards. Regarding the availability of network state information, we propose non-uniform pricing and uniform pricing strategies, respectively. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. To tackle the issue of energy constraints in IoMT devices, we adopt Lyapunov optimization for adaptive client selection, ensuring sustainable device participation in the FL process over time. In addition, our approach integrates DP to protect patient data, carefully balancing between privacy and the accuracy of the learning model. Our extensive simulations demonstrate marked improvements in privacy preservation, communication efficiency, and energy management efficiency, highlighting the effectiveness of our proposed method over existing solutions.


Keywordsdata modelsgame theoryresource allocation

Free keywordsfederated learning; internet of medical things (IoMT); differential privacy; game theory; client selection; resource allocation


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Ministry reportingYes

VIRTA submission year2025

Preliminary JUFO rating3


Last updated on 2025-25-01 at 20:06