A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

Liu, Linjie; Zhang, Jixian; Wang, Zhemin; Xu, Jia

doi:10.3390/math11244968

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A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

School of Information Science and Engineering, Yunnan University, Kunming 650504, China

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Mathematics 2023, 11(24), 4968; https://doi.org/10.3390/math11244968

Submission received: 7 November 2023 / Revised: 9 December 2023 / Accepted: 12 December 2023 / Published: 15 December 2023

(This article belongs to the Special Issue Operations Research and Optimization: Mathematical Methods and Applications)

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Abstract

Federated learning is a promising technique in cloud computing and edge computing environments, and designing a reasonable resource allocation scheme for federated learning is particularly important. In this paper, we propose an auction mechanism for federated learning resource allocation in the edge–cloud collaborative environment, which can motivate data owners to participate in federated learning and effectively utilize the resources and computing power of edge servers, thereby reducing the pressure on cloud services. Specifically, we formulate the federated learning platform data value maximization problem as an integer programming model with multiple constraints, develop a resource allocation algorithm based on the monotone submodular value function, devise a payment algorithm based on critical price theory and demonstrate that the mechanism satisfies truthfulness and individual rationality.

Keywords: reverse auction mechanism; resource allocation; federated learning; utility maximization

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MDPI and ACS Style

Liu, L.; Zhang, J.; Wang, Z.; Xu, J. A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration. Mathematics 2023, 11, 4968. https://doi.org/10.3390/math11244968

AMA Style

Liu L, Zhang J, Wang Z, Xu J. A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration. Mathematics. 2023; 11(24):4968. https://doi.org/10.3390/math11244968

Chicago/Turabian Style

Liu, Linjie, Jixian Zhang, Zhemin Wang, and Jia Xu. 2023. "A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration" Mathematics 11, no. 24: 4968. https://doi.org/10.3390/math11244968

APA Style

Liu, L., Zhang, J., Wang, Z., & Xu, J. (2023). A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration. Mathematics, 11(24), 4968. https://doi.org/10.3390/math11244968

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

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