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arXiv · 2410.05354

Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint

Yifan Wang·Cheng Zhang·Yuanndon Zhuang·Mingzeng Dai·Haiming Wang·Yongming Huang
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Abstract

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.

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Yifan Wang, Cheng Zhang, Yuanndon Zhuang, Mingzeng Dai, Haiming Wang, Yongming Huang. 2024-10-07. Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint. https://arxiv.org/abs/2410.05354

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