Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges
The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given stringent privacy regulations, future network architectures should use privacy-preserved ML for their applica...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10786352/ |
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| author | Chamara Sandeepa Engin Zeydan Tharaka Samarasinghe Madhusanka Liyanage |
| author_facet | Chamara Sandeepa Engin Zeydan Tharaka Samarasinghe Madhusanka Liyanage |
| author_sort | Chamara Sandeepa |
| collection | DOAJ |
| description | The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given stringent privacy regulations, future network architectures should use privacy-preserved ML for their applications and services. Federated Learning (FL) is expected to play an important role as a popular approach for distributed ML, as it protects privacy by design. However, many practical challenges exist before FL can be fully utilized as a key technology for these future networks. We consider the vision of a 6G layered architecture to evaluate the applicability of FL-based distributed intelligence. In this paper, we highlight the benefits of using FL for 6G and the main challenges and issues involved. We also discuss the existing solutions and the possible future directions that should be taken toward more robust and trustworthy FL for future networks. |
| format | Article |
| id | doaj-art-e896a14b17624cb697e266a19a34df4e |
| institution | OA Journals |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-e896a14b17624cb697e266a19a34df4e2025-08-20T02:27:49ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0169012910.1109/OJCOMS.2024.351383210786352Federated Learning for 6G Networks: Navigating Privacy Benefits and ChallengesChamara Sandeepa0https://orcid.org/0000-0002-3101-7097Engin Zeydan1Tharaka Samarasinghe2https://orcid.org/0000-0002-5189-5743Madhusanka Liyanage3https://orcid.org/0000-0003-4786-030XSchool of Computer Science, University College Dublin, Dublin 4, IrelandServices as Networks (SaS) Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya, Barcelona, SpainDepartment of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa, Sri LankaSchool of Computer Science, University College Dublin, Dublin 4, IrelandThe upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given stringent privacy regulations, future network architectures should use privacy-preserved ML for their applications and services. Federated Learning (FL) is expected to play an important role as a popular approach for distributed ML, as it protects privacy by design. However, many practical challenges exist before FL can be fully utilized as a key technology for these future networks. We consider the vision of a 6G layered architecture to evaluate the applicability of FL-based distributed intelligence. In this paper, we highlight the benefits of using FL for 6G and the main challenges and issues involved. We also discuss the existing solutions and the possible future directions that should be taken toward more robust and trustworthy FL for future networks.https://ieeexplore.ieee.org/document/10786352/Privacyfederated learning6Gbeyond 5GAIdistributed learning |
| spellingShingle | Chamara Sandeepa Engin Zeydan Tharaka Samarasinghe Madhusanka Liyanage Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges IEEE Open Journal of the Communications Society Privacy federated learning 6G beyond 5G AI distributed learning |
| title | Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges |
| title_full | Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges |
| title_fullStr | Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges |
| title_full_unstemmed | Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges |
| title_short | Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges |
| title_sort | federated learning for 6g networks navigating privacy benefits and challenges |
| topic | Privacy federated learning 6G beyond 5G AI distributed learning |
| url | https://ieeexplore.ieee.org/document/10786352/ |
| work_keys_str_mv | AT chamarasandeepa federatedlearningfor6gnetworksnavigatingprivacybenefitsandchallenges AT enginzeydan federatedlearningfor6gnetworksnavigatingprivacybenefitsandchallenges AT tharakasamarasinghe federatedlearningfor6gnetworksnavigatingprivacybenefitsandchallenges AT madhusankaliyanage federatedlearningfor6gnetworksnavigatingprivacybenefitsandchallenges |