A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration
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 edg...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/24/4968 |
| _version_ | 1797380159381176320 |
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| author | Linjie Liu Jixian Zhang Zhemin Wang Jia Xu |
| author_facet | Linjie Liu Jixian Zhang Zhemin Wang Jia Xu |
| author_sort | Linjie Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T20:34:18Z |
| format | Article |
| id | doaj.art-e1f2df7f957840c494fe58db148e8def |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-08T20:34:18Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-e1f2df7f957840c494fe58db148e8def2023-12-22T14:23:27ZengMDPI AGMathematics2227-73902023-12-011124496810.3390/math11244968A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud CollaborationLinjie Liu0Jixian Zhang1Zhemin Wang2Jia Xu3School of Information Science and Engineering, Yunnan University, Kunming 650504, ChinaSchool of Information Science and Engineering, Yunnan University, Kunming 650504, ChinaSchool of Information Science and Engineering, Yunnan University, Kunming 650504, ChinaSchool of Information Science and Engineering, Yunnan University, Kunming 650504, ChinaFederated 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.https://www.mdpi.com/2227-7390/11/24/4968reverse auction mechanismresource allocationfederated learningutility maximization |
| spellingShingle | Linjie Liu Jixian Zhang Zhemin Wang Jia Xu A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration Mathematics reverse auction mechanism resource allocation federated learning utility maximization |
| title | A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration |
| title_full | A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration |
| title_fullStr | A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration |
| title_full_unstemmed | A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration |
| title_short | A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration |
| title_sort | truthful reverse auction mechanism for federated learning utility maximization resource allocation in edge cloud collaboration |
| topic | reverse auction mechanism resource allocation federated learning utility maximization |
| url | https://www.mdpi.com/2227-7390/11/24/4968 |
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