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http://bura.brunel.ac.uk/handle/2438/25939Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Liu, J | - |
| dc.contributor.author | Zhang, G | - |
| dc.contributor.author | Wang, K | - |
| dc.contributor.author | Yang, K | - |
| dc.date.accessioned | 2023-02-08T17:52:20Z | - |
| dc.date.available | 2023-02-08T17:52:20Z | - |
| dc.date.issued | 2022-09-28 | - |
| dc.identifier | ORCID iDs: Jiawei Liu https://orcid.org/0000-0002-0532-6697; Guopeng Zhang https://orcid.org/0000-0001-7524-3144; Kezhi Wang https://orcid.org/0000-0001-8602-0800; Kun Yang https://orcid.org/0000-0002-6782-6689. | - |
| dc.identifier.citation | Liu, J. et al. (2023) 'Task-Load-Aware Game-Theoretic Framework for Wireless Federated Learning', IEEE Communications Letters, 27 (1), pp. 268 - 272. doi: 10.1109/LCOMM.2022.3210604. | en_US |
| dc.identifier.issn | 1089-7798 | - |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25939 | - |
| dc.description.sponsorship | 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61971421 and 62132004); Quzhou Government (Grant Number: 2021D003); Sichuan Major R&D Project (Grant Number: 22QYCX0168). | en_US |
| dc.format.extent | 268 - 272 | - |
| dc.format.medium | Print-Electronic | - |
| dc.language | English | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.rights | Copyright © 2022 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See: https://www.ieee.org/publications/rights/rights-policies.html | - |
| dc.rights.uri | https://www.ieee.org/publications/rights/rights-policies.html | - |
| dc.subject | machine learning | en_US |
| dc.subject | federated learning | en_US |
| dc.subject | resource allocation | en_US |
| dc.subject | Bertrand game | en_US |
| dc.subject | Nash equilibrium | en_US |
| dc.title | Task-Load-Aware Game-Theoretic Framework for Wireless Federated Learning | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | https://doi.org/10.1109/LCOMM.2022.3210604 | - |
| dc.relation.isPartOf | IEEE Communications Letters | - |
| pubs.issue | 1 | - |
| pubs.publication-status | Published | - |
| pubs.volume | 27 | - |
| dc.identifier.eissn | 1558-2558 | - |
| dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
| Appears in Collections: | Dept of Computer Science Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2022 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See: https://www.ieee.org/publications/rights/rights-policies.html | 353.34 kB | Adobe PDF | View/Open |
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