Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint
<|reference_start|>Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint: 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.<|reference_end|>
@article{wang2024over-the-air,
    title={Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power
  Constraint},
    author={Yifan Wang, Cheng Zhang, Yuanndon Zhuang, Mingzeng Dai, Haiming Wang,
  Yongming Huang},
    journal={arXiv preprint arXiv:2410.05354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05354},
    primaryClass={cs.LG cs.AI}
}