  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.
