  We propose a data-driven approach for power allocation in the context of
federated learning (FL) over interference-limited wireless networks. The power
policy is designed to maximize the transmitted information during the FL
process under communication constraints, with the ultimate objective of
improving the accuracy and efficiency of the global FL model being trained. The
proposed power allocation policy is parameterized using a graph convolutional
network and the associated constrained optimization problem is solved through a
primal-dual algorithm. Numerical experiments show that the proposed method
outperforms three baseline methods in both transmission success rate and FL
global performance.
