论文标题
查尔斯：无线网络上的渠道质量自适应的无线联邦学习
CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks
论文作者
论文摘要
空中联邦学习（OTA-FL）已成为一种有效的机制，可利用无线介质的叠加特性，并执行模型聚集，以实现空中的联合学习。 OTA-FL对无线通道褪色自然敏感，这可能会大大降低其学习准确性。为了应对这一挑战，在本文中，我们提出了一种称为Charles（通道质量吸引的局部估计和扩展）的OTA-FL算法。我们的Charles算法执行通道状态信息（CSI）估计和自适应缩放，以减轻无线通道褪色的影响。我们建立了查尔斯的理论收敛速度性能，并分析了CSI误差对查尔斯收敛的影响。我们表明，在CSI场景不完美的情况下，Charles中的自适应通道倒数缩放方案是可靠的。我们还通过数值结果证明，在CSI不完美的CSI下，Charles用异质数据优于现有的OTA-FL算法。
Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that exploits the superposition property of the wireless medium and performs model aggregation for federated learning in the air. OTA-FL is naturally sensitive to wireless channel fading, which could significantly diminish its learning accuracy. To address this challenge, in this paper, we propose an OTA-FL algorithm called CHARLES (channel-quality-aware over-the-air local estimating and scaling). Our CHARLES algorithm performs channel state information (CSI) estimation and adaptive scaling to mitigate the impacts of wireless channel fading. We establish the theoretical convergence rate performance of CHARLES and analyze the impacts of CSI error on the convergence of CHARLES. We show that the adaptive channel inversion scaling scheme in CHARLES is robust under imperfect CSI scenarios. We also demonstrate through numerical results that CHARLES outperforms existing OTA-FL algorithms with heterogeneous data under imperfect CSI.
