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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2410.05354 |
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| _version_ | 1866912083026640896 |
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| author | Wang, Yifan Zhang, Cheng Zhuang, Yuanndon Dai, Mingzeng Wang, Haiming Huang, Yongming |
| author_facet | Wang, Yifan Zhang, Cheng Zhuang, Yuanndon Dai, Mingzeng Wang, Haiming Huang, Yongming |
| contents | Wireless networks supporting artificial intelligence have gained significant attention, with Over-the-Air Federated Learning emerging as a key application due to its unique transmission and distributed computing characteristics. This paper derives error bounds for Over-the-Air Federated Learning in a Cell-free MIMO system and formulates an optimization problem to minimize optimality gap via joint optimization of power control and beamforming. We introduce the MOP-LOFPC algorithm, which employs Lyapunov optimization to decouple long-term constraints across rounds while requiring only causal channel state information. Experimental results demonstrate that MOP-LOFPC achieves a better and more flexible trade-off between the model's training loss and adherence to long-term power constraints compared to existing baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint Wang, Yifan Zhang, Cheng Zhuang, Yuanndon Dai, Mingzeng Wang, Haiming Huang, Yongming Machine Learning Artificial Intelligence Wireless networks supporting artificial intelligence have gained significant attention, with Over-the-Air Federated Learning emerging as a key application due to its unique transmission and distributed computing characteristics. This paper derives error bounds for Over-the-Air Federated Learning in a Cell-free MIMO system and formulates an optimization problem to minimize optimality gap via joint optimization of power control and beamforming. We introduce the MOP-LOFPC algorithm, which employs Lyapunov optimization to decouple long-term constraints across rounds while requiring only causal channel state information. Experimental results demonstrate that MOP-LOFPC achieves a better and more flexible trade-off between the model's training loss and adherence to long-term power constraints compared to existing baselines. |
| title | Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.05354 |