Low Complexity Byzantine-Resilient Federated Learning
<p dir="ltr">Federated learning (FL) has gained attention for enabling efficient distributed learning while maintaining data privacy. However, the data privacy constraint reduces the transparency in the agents’ model update making the learning process vulnerable to Byzantine attacks....
محفوظ في:
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513543182745600 |
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| author | A. Gouissem (17541396) |
| author2 | S. Hassanein (21399926) K. Abualsaud (17541399) E. Yaacoub (17541402) M. Mabrok (21399929) M. Abdallah (812014) T. Khattab (17541405) M. Guizani (17541408) |
| author2_role | author author author author author author author |
| author_facet | A. Gouissem (17541396) S. Hassanein (21399926) K. Abualsaud (17541399) E. Yaacoub (17541402) M. Mabrok (21399929) M. Abdallah (812014) T. Khattab (17541405) M. Guizani (17541408) |
| author_role | author |
| dc.creator.none.fl_str_mv | A. Gouissem (17541396) S. Hassanein (21399926) K. Abualsaud (17541399) E. Yaacoub (17541402) M. Mabrok (21399929) M. Abdallah (812014) T. Khattab (17541405) M. Guizani (17541408) |
| dc.date.none.fl_str_mv | 2024-11-28T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/tifs.2024.3482727 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Low_Complexity_Byzantine-Resilient_Federated_Learning/29605283 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Mathematical sciences Numerical and computational mathematics Statistics Federated learning Distributed learning Byzantine attacks Convergence analysis |
| dc.title.none.fl_str_mv | Low Complexity Byzantine-Resilient Federated Learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Federated learning (FL) has gained attention for enabling efficient distributed learning while maintaining data privacy. However, the data privacy constraint reduces the transparency in the agents’ model update making the learning process vulnerable to Byzantine attacks. In this paper, a mathematical proof is provided to show that when the traditional model-combining scheme is used, the model will eventually diverge to non-useful solutions in the presence of Byzantine agents independently from their number or their contributions. A low complexity norm-control based aggregation approach is also proposed and shown to converge to the optimal and sub-optimal solutions in the absence or presence of Byzantine nodes, respectively. Monte-Carlo simulations are also conducted to verify and validate the mathematical derivations and the efficiency of the proposed approach in protecting the FL model.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Information Forensics and Security<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tifs.2024.3482727" target="_blank">https://dx.doi.org/10.1109/tifs.2024.3482727</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1634166746de32beaa6953e1b6023aae |
| identifier_str_mv | 10.1109/tifs.2024.3482727 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29605283 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Low Complexity Byzantine-Resilient Federated LearningA. Gouissem (17541396)S. Hassanein (21399926)K. Abualsaud (17541399)E. Yaacoub (17541402)M. Mabrok (21399929)M. Abdallah (812014)T. Khattab (17541405)M. Guizani (17541408)Information and computing sciencesArtificial intelligenceDistributed computing and systems softwareMachine learningMathematical sciencesNumerical and computational mathematicsStatisticsFederated learningDistributed learningByzantine attacksConvergence analysis<p dir="ltr">Federated learning (FL) has gained attention for enabling efficient distributed learning while maintaining data privacy. However, the data privacy constraint reduces the transparency in the agents’ model update making the learning process vulnerable to Byzantine attacks. In this paper, a mathematical proof is provided to show that when the traditional model-combining scheme is used, the model will eventually diverge to non-useful solutions in the presence of Byzantine agents independently from their number or their contributions. A low complexity norm-control based aggregation approach is also proposed and shown to converge to the optimal and sub-optimal solutions in the absence or presence of Byzantine nodes, respectively. Monte-Carlo simulations are also conducted to verify and validate the mathematical derivations and the efficiency of the proposed approach in protecting the FL model.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Information Forensics and Security<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tifs.2024.3482727" target="_blank">https://dx.doi.org/10.1109/tifs.2024.3482727</a></p>2024-11-28T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/tifs.2024.3482727https://figshare.com/articles/journal_contribution/Low_Complexity_Byzantine-Resilient_Federated_Learning/29605283CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296052832024-11-28T03:00:00Z |
| spellingShingle | Low Complexity Byzantine-Resilient Federated Learning A. Gouissem (17541396) Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Mathematical sciences Numerical and computational mathematics Statistics Federated learning Distributed learning Byzantine attacks Convergence analysis |
| status_str | publishedVersion |
| title | Low Complexity Byzantine-Resilient Federated Learning |
| title_full | Low Complexity Byzantine-Resilient Federated Learning |
| title_fullStr | Low Complexity Byzantine-Resilient Federated Learning |
| title_full_unstemmed | Low Complexity Byzantine-Resilient Federated Learning |
| title_short | Low Complexity Byzantine-Resilient Federated Learning |
| title_sort | Low Complexity Byzantine-Resilient Federated Learning |
| topic | Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Mathematical sciences Numerical and computational mathematics Statistics Federated learning Distributed learning Byzantine attacks Convergence analysis |