We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks. The proposed method is useful in challenging scenarios where the wireless channel is changing during the FL training process and when the training data are not independent and identically distributed (non-i.i.d.) on the local devices. Intuitively, the power policy is designed to optimize the information received at the server end during the FL process under communication constraints. Ultimately, our goal is to improve the accuracy and efficiency of the global FL model being trained. The proposed power allocation policy is parameterized using graph convolutional networks (GCNs), and the associated constrained optimization problem is solved through a primal-dual (PD) algorithm. Theoretically, we show that the formulated problem has a zero duality gap and, once the power policy is parameterized, optimality depends on how expressive this parameterization is. Numerically, we demonstrate that the proposed method outperforms existing baselines under different wireless channel settings and varying degrees of data heterogeneity.

翻译：我们提出了一种新颖的数据驱动方法，用于在干扰受限的无线网络中为联邦学习(FL)分配传输功率。该方法在FL训练过程中无线信道动态变化、且本地设备训练数据非独立同分布(non-i.i.d.)的挑战性场景中尤为有效。直观上，功率策略旨在通信约束条件下优化FL过程中服务器端接收的信息。最终目标是提升所训练的全局FL模型的精度与效率。所提出的功率分配策略采用图卷积网络(GCN)进行参数化，并通过原始-对偶(PD)算法求解相应的约束优化问题。理论上，我们证明了所构建问题具有零对偶间隙，且一旦功率策略完成参数化，其最优性取决于参数化表达能力的强弱。数值实验表明，在不同无线信道设置和数据异质性程度下，该方法优于现有基线方法。