Power Allocation for Wireless Federated Learning using Graph Neural Networks
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.
Code (1)
Tasks
Federated LearningSimilar Papers 제목 키워드 기반
Learning to Transmit with Provable Guarantees in Wireless Federated Learning
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 channe…
Federated LearningA Joint Learning and Communications Framework for Federated Learning over Wireless Networks
In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their …
Federated LearningScheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC
Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive for bandwidth limited applications espec…
BIG-bench Machine LearningFederated LearningSchedulingGNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks
The optimal allocation of channels and power resources plays a crucial role in ensuring minimal interference, maximal data rates, and efficient energy utilisation. As a successful approach for tackling resource managemen…
Computational EfficiencyGraph Neural NetworkManagementFederated Learning over Wireless IoT Networks with Optimized Communication and Resources
To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model training. Federated learning (FL), as a para…
Federated LearningScheduling