Graph Neural Network-based Federated Learning for Sum-rate Maximization in Small-cell Wireless Network
Hieu Nguyen Doan, Nguyen Xuan Tung, Quang Vinh, Won–Joo Hwang, Sang–Hwa Chung, Jong Deok Kim · 2023
This paper investigates the scalability ability of Graph Neural Network (GNN) for solving resource allocation problems in wireless networks. Although GNNs are able to work on diverse network settings, the performance significantly decreases on unseen network settings. To overcome this issue, we propose the combination of GNN and Federated Learning (FL) framework, namely GraphFL. To illustrate the proposed framework, we consider the power control optimization problems of a small-cell wireless system, where each access point at each cell trains a local GNN model to manage the power allocation for its own users. A global GNN model is aggregated from local models following the FL procedure. Thereby, the global GNN is able to work on diverse network settings. Experimental results demonstrate the adaptability of the GNN model on both seen and unseen network settings.