Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913452143935488 |
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| author | Fang, Zexin Han, Bin Schotten, Hans D. |
| author_facet | Fang, Zexin Han, Bin Schotten, Hans D. |
| contents | Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching attacks with various adversarial attacks or deployment tactics. In this paper, we analyze such vulnerabilities, corresponding solutions were brought forth, and validated through simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03088 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation Fang, Zexin Han, Bin Schotten, Hans D. Machine Learning Artificial Intelligence Networking and Internet Architecture Signal Processing Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching attacks with various adversarial attacks or deployment tactics. In this paper, we analyze such vulnerabilities, corresponding solutions were brought forth, and validated through simulation. |
| title | Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation |
| topic | Machine Learning Artificial Intelligence Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2404.03088 |