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

Linjie Liu, Jixian Zhang (), Zhemin Wang and Jia Xu
Additional contact information
Linjie Liu: School of Information Science and Engineering, Yunnan University, Kunming 650504, China
Jixian Zhang: School of Information Science and Engineering, Yunnan University, Kunming 650504, China
Zhemin Wang: School of Information Science and Engineering, Yunnan University, Kunming 650504, China
Jia Xu: School of Information Science and Engineering, Yunnan University, Kunming 650504, China

Mathematics, 2023, vol. 11, issue 24, 1-18

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/24/4968/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/24/4968/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:24:p:4968-:d:1301053

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().