Energy-Efficient and Privacy-Preserved Incentive Mechanism for Mobile Edge Computing-Assisted Federated Learning in Healthcare System

Recent advancements in the Internet of Medical Things (IoMT) have significantly influenced the development of smart healthcare systems. Mobile edge computing (MEC)-assisted federated learning (FL) has emerged as a promising technology for providing fast, efficient, and reliable healthcare services w...

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Published inIEEE eTransactions on network and service management Vol. 21; no. 4; pp. 4801 - 4815
Main Authors Liu, Jingyuan, Chang, Zheng, Wang, Kai, Zhao, Zhiwei, Hamalainen, Timo
Format Journal Article
LanguageEnglish
Published IEEE 01.08.2024
Subjects
Computational modeling
Data models
Data privacy
energy efficiency
Federated learning
Games
healthcare
incentive mechanism
Medical services
mobile edge computing
power allocation
Privacy
privacy-preserving
Servers
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Abstract Recent advancements in the Internet of Medical Things (IoMT) have significantly influenced the development of smart healthcare systems. Mobile edge computing (MEC)-assisted federated learning (FL) has emerged as a promising technology for providing fast, efficient, and reliable healthcare services while ensuring patient privacy. However, concerns about the privacy and security of sensitive information often make patients hesitant to share their data. Moreover, MEC servers face challenges accessing the necessary radio resources for data transmission. To address these issues, designing an effective incentive mechanism that encourages healthcare user participation in FL and facilitates resource provision from the base station (BS) is vital. This work proposes an efficient and privacy-preserving incentive scheme that considers the interaction among the BS, MEC servers, and MEC users in the MEC-assisted FL healthcare system. Utilizing the Stackelberg game model, we investigate the allocation of transmit power, determination of differential privacy (DP) budgets for MEC users, reward strategies, radio resource demands for MEC servers, and pricing for radio resources at the BS. Furthermore, we analyze the Stackelberg equilibrium and empirically validate the effectiveness of our proposed scheme using a real-world medical dataset.
AbstractList Recent advancements in the Internet of Medical Things (IoMT) have significantly influenced the development of smart healthcare systems. Mobile edge computing (MEC)-assisted federated learning (FL) has emerged as a promising technology for providing fast, efficient, and reliable healthcare services while ensuring patient privacy. However, concerns about the privacy and security of sensitive information often make patients hesitant to share their data. Moreover, MEC servers face challenges accessing the necessary radio resources for data transmission. To address these issues, designing an effective incentive mechanism that encourages healthcare user participation in FL and facilitates resource provision from the base station (BS) is vital. This work proposes an efficient and privacy-preserving incentive scheme that considers the interaction among the BS, MEC servers, and MEC users in the MEC-assisted FL healthcare system. Utilizing the Stackelberg game model, we investigate the allocation of transmit power, determination of differential privacy (DP) budgets for MEC users, reward strategies, radio resource demands for MEC servers, and pricing for radio resources at the BS. Furthermore, we analyze the Stackelberg equilibrium and empirically validate the effectiveness of our proposed scheme using a real-world medical dataset.
Author Liu, Jingyuan
Hamalainen, Timo
Chang, Zheng
Wang, Kai
Zhao, Zhiwei
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Snippet Recent advancements in the Internet of Medical Things (IoMT) have significantly influenced the development of smart healthcare systems. Mobile edge computing...
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SubjectTerms Computational modeling
Data models
Data privacy
energy efficiency
Federated learning
Games
healthcare
incentive mechanism
Medical services
mobile edge computing
power allocation
Privacy
privacy-preserving
Servers
Title Energy-Efficient and Privacy-Preserved Incentive Mechanism for Mobile Edge Computing-Assisted Federated Learning in Healthcare System
URI https://ieeexplore.ieee.org/document/10557646
Volume 21
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