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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.00011 |
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| _version_ | 1866916636132376576 |
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| author | Zhao, Yang Xu, Minrui Wang, Ping Niyato, Dusit |
| author_facet | Zhao, Yang Xu, Minrui Wang, Ping Niyato, Dusit |
| contents | Over-the-air (OTA) federated learning (FL) effectively utilizes communication bandwidth, yet it is vulnerable to errors during analog aggregation. While removing users with unfavorable channel conditions can mitigate these errors, it also reduces the available local training data for FL, which in turn hinders the convergence rate of the training process. To tackle this issue, we propose using fluid antenna (FA) techniques to enhance the degrees of freedom within the channel space, ultimately boosting the convergence speed of FL training. Moreover, we develop a novel approach that effectively coordinates uplink receiver beamforming, user selection, and FA positioning to optimize the convergence rate of OTA FL training in dynamic wireless environments. We address this challenging stochastic optimization by reformulating it as a mixed-integer programming problem by utilizing the training loss upper bound. We then introduce a penalty dual decomposition (PDD) method to solve the mixed-integer mixed programming problem. Experimental results indicate that incorporating FA techniques significantly accelerates the training convergence of FL and greatly surpasses conventional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection Zhao, Yang Xu, Minrui Wang, Ping Niyato, Dusit Signal Processing Over-the-air (OTA) federated learning (FL) effectively utilizes communication bandwidth, yet it is vulnerable to errors during analog aggregation. While removing users with unfavorable channel conditions can mitigate these errors, it also reduces the available local training data for FL, which in turn hinders the convergence rate of the training process. To tackle this issue, we propose using fluid antenna (FA) techniques to enhance the degrees of freedom within the channel space, ultimately boosting the convergence speed of FL training. Moreover, we develop a novel approach that effectively coordinates uplink receiver beamforming, user selection, and FA positioning to optimize the convergence rate of OTA FL training in dynamic wireless environments. We address this challenging stochastic optimization by reformulating it as a mixed-integer programming problem by utilizing the training loss upper bound. We then introduce a penalty dual decomposition (PDD) method to solve the mixed-integer mixed programming problem. Experimental results indicate that incorporating FA techniques significantly accelerates the training convergence of FL and greatly surpasses conventional methods. |
| title | Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.00011 |