Learning to Transmit with Provable Guarantees in Wireless Federated Learning
This addresses the challenge of efficient and reliable wireless communication for federated learning, particularly in scenarios with data heterogeneity and channel variability, representing an incremental improvement over prior methods.
The authors tackled the problem of transmit power allocation for federated learning in interference-limited wireless networks with non-i.i.d. data and dynamic channels, proposing a GCN-based method that outperforms existing baselines in accuracy and efficiency under various settings.
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.