Collaborative Byzantine Resilient Federated Learning
<p dir="ltr">Federated learning (FL) enables an effective and private distributed learning process. However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The first purpose of this work is to demonstrate mathematically that the traditional arithmetic...
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2023
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| _version_ | 1864513535663407104 |
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| author | A. Gouissem (17541396) |
| author2 | K. Abualsaud (17541399) E. Yaacoub (17541402) T. Khattab (17541405) M. Guizani (17541408) |
| author2_role | author author author author |
| author_facet | A. Gouissem (17541396) K. Abualsaud (17541399) E. Yaacoub (17541402) T. Khattab (17541405) M. Guizani (17541408) |
| author_role | author |
| dc.creator.none.fl_str_mv | A. Gouissem (17541396) K. Abualsaud (17541399) E. Yaacoub (17541402) T. Khattab (17541405) M. Guizani (17541408) |
| dc.date.none.fl_str_mv | 2023-04-11T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/jiot.2023.3266347 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Collaborative_Byzantine_Resilient_Federated_Learning/24717186 |
| 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 Data management and data science Distributed computing and systems software Machine learning Training Data models Convergence Computational modeling Internet of Things Servers Predictive models |
| dc.title.none.fl_str_mv | Collaborative 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) enables an effective and private distributed learning process. However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The first purpose of this work is to demonstrate mathematically that the traditional arithmetic-averaging model-combining approach will ultimately diverge to an unstable solution in the presence of Byzantine agents. This article also proposes a low-complexity, decentralized Byzantine resilient training mechanism. The proposed technique identifies and isolates hostile nodes rather than just mitigating their impact on the global model. In addition, the suggested approach may be used alone or in conjunction with other protection techniques to provide an additional layer of security in the event of misdetection. The suggested solution is decentralized, allowing all participating nodes to jointly identify harmful individuals using a novel cross check mechanism. To prevent biased assessments, the identification procedure is done blindly and is incorporated into the regular training process. A smart activation mechanism based on flag activation is also proposed to reduce the network overhead. Finally, general mathematical proofs combined with extensive experimental results applied in a healthcare electrocardiogram (ECG) monitoring scenario show that the proposed techniques are very efficient at accurately predicting heart problems.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Internet of Things Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" 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/jiot.2023.3266347" target="_blank">https://dx.doi.org/10.1109/jiot.2023.3266347</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a8b82cb60e405a7ba54e2c46f55b5dbe |
| identifier_str_mv | 10.1109/jiot.2023.3266347 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717186 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Collaborative Byzantine Resilient Federated LearningA. Gouissem (17541396)K. Abualsaud (17541399)E. Yaacoub (17541402)T. Khattab (17541405)M. Guizani (17541408)Information and computing sciencesData management and data scienceDistributed computing and systems softwareMachine learningTrainingData modelsConvergenceComputational modelingInternet of ThingsServersPredictive models<p dir="ltr">Federated learning (FL) enables an effective and private distributed learning process. However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The first purpose of this work is to demonstrate mathematically that the traditional arithmetic-averaging model-combining approach will ultimately diverge to an unstable solution in the presence of Byzantine agents. This article also proposes a low-complexity, decentralized Byzantine resilient training mechanism. The proposed technique identifies and isolates hostile nodes rather than just mitigating their impact on the global model. In addition, the suggested approach may be used alone or in conjunction with other protection techniques to provide an additional layer of security in the event of misdetection. The suggested solution is decentralized, allowing all participating nodes to jointly identify harmful individuals using a novel cross check mechanism. To prevent biased assessments, the identification procedure is done blindly and is incorporated into the regular training process. A smart activation mechanism based on flag activation is also proposed to reduce the network overhead. Finally, general mathematical proofs combined with extensive experimental results applied in a healthcare electrocardiogram (ECG) monitoring scenario show that the proposed techniques are very efficient at accurately predicting heart problems.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Internet of Things Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" 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/jiot.2023.3266347" target="_blank">https://dx.doi.org/10.1109/jiot.2023.3266347</a></p>2023-04-11T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/jiot.2023.3266347https://figshare.com/articles/journal_contribution/Collaborative_Byzantine_Resilient_Federated_Learning/24717186CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247171862023-04-11T06:00:00Z |
| spellingShingle | Collaborative Byzantine Resilient Federated Learning A. Gouissem (17541396) Information and computing sciences Data management and data science Distributed computing and systems software Machine learning Training Data models Convergence Computational modeling Internet of Things Servers Predictive models |
| status_str | publishedVersion |
| title | Collaborative Byzantine Resilient Federated Learning |
| title_full | Collaborative Byzantine Resilient Federated Learning |
| title_fullStr | Collaborative Byzantine Resilient Federated Learning |
| title_full_unstemmed | Collaborative Byzantine Resilient Federated Learning |
| title_short | Collaborative Byzantine Resilient Federated Learning |
| title_sort | Collaborative Byzantine Resilient Federated Learning |
| topic | Information and computing sciences Data management and data science Distributed computing and systems software Machine learning Training Data models Convergence Computational modeling Internet of Things Servers Predictive models |