Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929388940951552 |
|---|---|
| author | Vo, Viet Dayaratne, Thusitha Haydon, Blake Yuan, Xingliang Lai, Shangqi Abuadbba, Sharif Suzuki, Hajime Rudolph, Carsten |
| author_facet | Vo, Viet Dayaratne, Thusitha Haydon, Blake Yuan, Xingliang Lai, Shangqi Abuadbba, Sharif Suzuki, Hajime Rudolph, Carsten |
| contents | Spectrum sharing is increasingly vital in 6G wireless communication, facilitating dynamic access to unused spectrum holes. Recently, there has been a significant shift towards employing machine learning (ML) techniques for sensing spectrum holes. In this context, federated learning (FL)-enabled spectrum sensing technology has garnered wide attention, allowing for the construction of an aggregated ML model without disclosing the private spectrum sensing information of wireless user devices. However, the integrity of collaborative training and the privacy of spectrum information from local users have remained largely unexplored. This article first examines the latest developments in FL-enabled spectrum sharing for prospective 6G scenarios. It then identifies practical attack vectors in 6G to illustrate potential AI-powered security and privacy threats in these contexts. Finally, the study outlines future directions, including practical defense challenges and guidelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing Vo, Viet Dayaratne, Thusitha Haydon, Blake Yuan, Xingliang Lai, Shangqi Abuadbba, Sharif Suzuki, Hajime Rudolph, Carsten Cryptography and Security Distributed, Parallel, and Cluster Computing Emerging Technologies Machine Learning Networking and Internet Architecture Spectrum sharing is increasingly vital in 6G wireless communication, facilitating dynamic access to unused spectrum holes. Recently, there has been a significant shift towards employing machine learning (ML) techniques for sensing spectrum holes. In this context, federated learning (FL)-enabled spectrum sensing technology has garnered wide attention, allowing for the construction of an aggregated ML model without disclosing the private spectrum sensing information of wireless user devices. However, the integrity of collaborative training and the privacy of spectrum information from local users have remained largely unexplored. This article first examines the latest developments in FL-enabled spectrum sharing for prospective 6G scenarios. It then identifies practical attack vectors in 6G to illustrate potential AI-powered security and privacy threats in these contexts. Finally, the study outlines future directions, including practical defense challenges and guidelines. |
| title | Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing Emerging Technologies Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2406.12330 |