在无蜂窝大规模MIMO中实现低功耗联邦学习，提升训练效率与能耗平衡。
Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint
- 用李雅普诺夫优化解耦多轮长期功率约束，仅需实时信道信息
- 相比基线方法，在模型损失与功率约束间实现更优权衡
- 适合资源受限的分布式智能无线系统部署
支持人工智能的无线网络受到广泛关注，其中空中联邦学习因其独特的传输与分布式计算特性成为关键应用。本文推导了无蜂窝大规模MIMO系统中空中联邦学习的误差界，并构建优化问题，通过联合优化功率控制与波束成形来最小化模型最优性差距。提出MOP-LOFPC算法，利用李雅普诺夫优化在多轮之间解耦长期功率约束，仅需因果信道状态信息。实验结果表明，该算法在模型训练损失与长期功率约束遵守之间实现了更优且更灵活的权衡，优于现有基线方法。
原文摘要 · Abstract (English)
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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