Stackelberg Game Based Resource Allocation Algorithm for Federated Learning in MEC Systems
Introducing Federated Learning (FL) into the mo- bile edge computing (MEC) system can effectively deal with delay-sensitive tasks and protect end devices (EDs) data privacy. In the process of participating in FL, the EDs will carry out a large number of local iterations and multiple rounds of commun...
Uloženo v:
| Vydáno v: | 2023 6th World Conference on Computing and Communication Technologies (WCCCT) s. 7 - 12 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
06.01.2023
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Introducing Federated Learning (FL) into the mo- bile edge computing (MEC) system can effectively deal with delay-sensitive tasks and protect end devices (EDs) data privacy. In the process of participating in FL, the EDs will carry out a large number of local iterations and multiple rounds of communication with the MEC server to achieve a target model accuracy. These will bring delay and energy cost which may reduce EDs' willingness to participate. In this paper, a resource allocation algorithm considering EDs incentives is proposed. We model the resource allocation of the MEC server and EDs as a two-layer Stackelberg game model and design two-layer utility functions. In EDs layer, we provide rewards to incentive EDs to contribute computing resource to achieve higher local model accuracy and weigh it against energy consumption of ED. In MEC server layer, the tradeoff between global model accuracy and system delay is conducted. We take utilities maximization as the optimization objective, and optimize the number of local iterations and bandwidth of EDs to achieve joint computing and communication resource allocation in the MEC system. Then, according to the solution of the optimization problems, we propose a resource allocation algorithm. Finally, the simulation results show that the proposed algorithm is superior to the benchmark schemes in reducing EDs' energy consumption and system delay, which can achieve the purpose of encouraging EDs to participate. |
|---|---|
| AbstractList | Introducing Federated Learning (FL) into the mo- bile edge computing (MEC) system can effectively deal with delay-sensitive tasks and protect end devices (EDs) data privacy. In the process of participating in FL, the EDs will carry out a large number of local iterations and multiple rounds of communication with the MEC server to achieve a target model accuracy. These will bring delay and energy cost which may reduce EDs' willingness to participate. In this paper, a resource allocation algorithm considering EDs incentives is proposed. We model the resource allocation of the MEC server and EDs as a two-layer Stackelberg game model and design two-layer utility functions. In EDs layer, we provide rewards to incentive EDs to contribute computing resource to achieve higher local model accuracy and weigh it against energy consumption of ED. In MEC server layer, the tradeoff between global model accuracy and system delay is conducted. We take utilities maximization as the optimization objective, and optimize the number of local iterations and bandwidth of EDs to achieve joint computing and communication resource allocation in the MEC system. Then, according to the solution of the optimization problems, we propose a resource allocation algorithm. Finally, the simulation results show that the proposed algorithm is superior to the benchmark schemes in reducing EDs' energy consumption and system delay, which can achieve the purpose of encouraging EDs to participate. |
| Author | Tang, Xiongyan Wang, Yue Chen, Gao Huang, Rong Wang, Liwen |
| Author_xml | – sequence: 1 givenname: Xiongyan surname: Tang fullname: Tang, Xiongyan email: tangxy@chinaunicom.cn organization: China Unicom Research Institute,Beijing,China – sequence: 2 givenname: Yue surname: Wang fullname: Wang, Yue email: ywang2018@bupt.edu.cn organization: Beijing University of Posts and Telecommunications,School of Information and Communication Engineering,Beijing,China – sequence: 3 givenname: Rong surname: Huang fullname: Huang, Rong email: huangr27@chinaunicom.cn organization: China Unicom Research Institute,Beijing,China – sequence: 4 givenname: Gao surname: Chen fullname: Chen, Gao email: cheng96@chinaunicom.cn organization: China Unicom Research Institute,Beijing,China – sequence: 5 givenname: Liwen surname: Wang fullname: Wang, Liwen email: wanglw97@chinaunicom.cn organization: China Unicom Research Institute,Beijing,China |
| BookMark | eNo1j81KxDAcxCPoQdd9Aw95ga35aL6Oa9ldhYrgrgheljT5twbbRNJ62Le3og4M8zsMA3OFzmOKgBCmpKCUmNvXqqoOQiohCkYYLyghglHFztDSKE2lFKWkpVSX6G0_WfcBfQO5wzs7AL6zI3j8DGP6yg7wuu-Ts1NIccYu5TC9D7hNGW_BQ7bT3K3B5hhih0PEj5sK70_jBMN4jS5a24-w_MsFetluDtX9qn7aPVTrehUoNdPKaKdpQ_WPlBQtQCkId7qlUmlujNWt14J7ORtI451RjhFOmGm1Lz3hC3TzuxsA4PiZw2Dz6fh_mX8DNR5Qew |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/WCCCT56755.2023.10052172 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9781665461467 1665461462 |
| EndPage | 12 |
| ExternalDocumentID | 10052172 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i119t-98c81b188888765fee4503c8f1678399a8fd853d653de0bdc97c203029f8d4d03 |
| IEDL.DBID | RIE |
| IngestDate | Thu Jan 18 11:14:34 EST 2024 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i119t-98c81b188888765fee4503c8f1678399a8fd853d653de0bdc97c203029f8d4d03 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10052172 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-Jan.-6 |
| PublicationDateYYYYMMDD | 2023-01-06 |
| PublicationDate_xml | – month: 01 year: 2023 text: 2023-Jan.-6 day: 06 |
| PublicationDecade | 2020 |
| PublicationTitle | 2023 6th World Conference on Computing and Communication Technologies (WCCCT) |
| PublicationTitleAbbrev | WCCCT |
| PublicationYear | 2023 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.8404019 |
| Snippet | Introducing Federated Learning (FL) into the mo- bile edge computing (MEC) system can effectively deal with delay-sensitive tasks and protect end devices (EDs)... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 7 |
| SubjectTerms | Computational modeling Delays Energy consumption Federated learning Games incentive mechanism mobile edge computing resource allocation Resource management Simulation |
| Title | Stackelberg Game Based Resource Allocation Algorithm for Federated Learning in MEC Systems |
| URI | https://ieeexplore.ieee.org/document/10052172 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFA46PHhSceJvcvDamaxtfhy1bHrQscPU4WVkycscuG7Mzb_fl6xVPHiwUAilUHjh9fvyku97hFxpnwrHQCWeS0iytheJcQYSXLc5BYIZEzfanx9kr6eGQ92vxOpRCwMA8fAZtMIw7uW7uV2HUhlmeJCaSvzjbkspNmKt-nQO09cvRVEMcmTAeSs0BW_Vr_9qnBJxo7v3zy_uk-aPAo_2v7HlgGxBeUhekRli0kVXKnpnZkBvEYMcrUvw9OY9QFMINQ4nc1z3v80oslLaDZYRyCodrfxUJ3Ra0sdOQSvH8iZ56nYGxX1S9UZIppzrVaKVRcLJVbikyD1AlrPUKs8RfZB0GOUdIrETeAMbO6ulbWNCt7VXLnMsPSKNcl7CMaE2BQvB1SfPTAaSj41U2vOx8ixnkMMJaYbAjBYb-4tRHZPTP56fkd0Q_linEOeksVqu4YLs2M_V9GN5GSftC7UNmdk |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEA1SBT2pWPHbHLxuTbofSY66tCq2pYeqxUtJk0kt2K3U1t_vJO4qHjy4sBAWloUJs-9lkveGkAvl4swykJHjAqKk6bJIWw0RrtushIxpHTbaHzui15PDoeqXYvWghQGAcPgMGn4Y9vLt3Kx8qQwz3EtNBf5x133rrFKuVZ3PYeryKc_zQYocOG34tuCN6oVfrVMCcrS3__nNHVL_0eDR_je67JI1KPbIM3JDTLvgS0Vv9AzoNaKQpVURnl69enDywcbhZI4r_5cZRV5K2940AnmlpaWj6oROC9pt5bT0LK-Th3ZrkN9GZXeEaMq5WkZKGqScXPpLZKkDSFIWG-k44g_SDi2dRSy2Gd7AxtYoYZqY0k3lpE0si_dJrZgXcECoicGA9_VJE52A4GMtpHJ8LB1LGaRwSOo-MKO3LwOMURWToz-en5PN20G3M-rc9e6PyZafilC1yE5IbblYwSnZMB_L6fviLEzgJ-oLnSI |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2023+6th+World+Conference+on+Computing+and+Communication+Technologies+%28WCCCT%29&rft.atitle=Stackelberg+Game+Based+Resource+Allocation+Algorithm+for+Federated+Learning+in+MEC+Systems&rft.au=Tang%2C+Xiongyan&rft.au=Wang%2C+Yue&rft.au=Huang%2C+Rong&rft.au=Chen%2C+Gao&rft.date=2023-01-06&rft.pub=IEEE&rft.spage=7&rft.epage=12&rft_id=info:doi/10.1109%2FWCCCT56755.2023.10052172&rft.externalDocID=10052172 |