A game incentive mechanism for energy efficient federated learning in computing power networks
Computing Power Network (CPN) is emerging as one of the important research interests in beyond 5G (B5G) or 6G. This paper constructs a CPN based on Federated Learning (FL), where all Multi-access Edge Computing (MEC) servers are linked to a computing power center via wireless links. Through this FL...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-12-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864823001566 |
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| author | Xiao Lin Ruolin Wu Haibo Mei Kun Yang |
| author_facet | Xiao Lin Ruolin Wu Haibo Mei Kun Yang |
| author_sort | Xiao Lin |
| collection | DOAJ |
| description | Computing Power Network (CPN) is emerging as one of the important research interests in beyond 5G (B5G) or 6G. This paper constructs a CPN based on Federated Learning (FL), where all Multi-access Edge Computing (MEC) servers are linked to a computing power center via wireless links. Through this FL procedure, each MEC server in CPN can independently train the learning models using localized data, thus preserving data privacy. However, it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers. To address these issues, we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers. Afterwards, we formulate a comprehensive algorithm to jointly optimize the communication resource (wireless bandwidth and transmission power) allocations and the computation resource (computation capacity of MEC servers) allocations while ensuring the local accuracy of the training of each MEC server. The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN. |
| format | Article |
| id | doaj-art-98e9ba5e184846f29d6cef5cf34ce090 |
| institution | Kabale University |
| issn | 2352-8648 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-98e9ba5e184846f29d6cef5cf34ce0902024-12-29T04:47:34ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482024-12-0110617411747A game incentive mechanism for energy efficient federated learning in computing power networksXiao Lin0Ruolin Wu1Haibo Mei2Kun Yang3School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaChengdu Tongfei Technology, Co., Ltd, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China; Corresponding author at: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaComputing Power Network (CPN) is emerging as one of the important research interests in beyond 5G (B5G) or 6G. This paper constructs a CPN based on Federated Learning (FL), where all Multi-access Edge Computing (MEC) servers are linked to a computing power center via wireless links. Through this FL procedure, each MEC server in CPN can independently train the learning models using localized data, thus preserving data privacy. However, it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers. To address these issues, we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers. Afterwards, we formulate a comprehensive algorithm to jointly optimize the communication resource (wireless bandwidth and transmission power) allocations and the computation resource (computation capacity of MEC servers) allocations while ensuring the local accuracy of the training of each MEC server. The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.http://www.sciencedirect.com/science/article/pii/S2352864823001566Computing power networkFederated learningEnergy efficiencyStackelberg gameResource allocation |
| spellingShingle | Xiao Lin Ruolin Wu Haibo Mei Kun Yang A game incentive mechanism for energy efficient federated learning in computing power networks Digital Communications and Networks Computing power network Federated learning Energy efficiency Stackelberg game Resource allocation |
| title | A game incentive mechanism for energy efficient federated learning in computing power networks |
| title_full | A game incentive mechanism for energy efficient federated learning in computing power networks |
| title_fullStr | A game incentive mechanism for energy efficient federated learning in computing power networks |
| title_full_unstemmed | A game incentive mechanism for energy efficient federated learning in computing power networks |
| title_short | A game incentive mechanism for energy efficient federated learning in computing power networks |
| title_sort | game incentive mechanism for energy efficient federated learning in computing power networks |
| topic | Computing power network Federated learning Energy efficiency Stackelberg game Resource allocation |
| url | http://www.sciencedirect.com/science/article/pii/S2352864823001566 |
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