Tackling Resource Allocation for Decentralized Federated Learning: A GNN-Based Approach

Abstract

Decentralized federated learning (DFL) enables clients to train a neural network model in a device-to-device (D2D) manner without central coordination. In practical systems, DFL faces challenges due to dynamic topology changes, time-varying channel conditions, and limited computational capability of the clients. These factors can affect the learning performance and efficiency of DFL. To address the aforementioned challenges, in this paper, we propose a graph neural network (GNN)–based algorithm to minimize the total delay and energy consumption on training and improve the learning performance of DFL in D2D wireless networks. In our proposed GNN, a multi-head graph attention mechanism is used to capture different features of clients and wireless channels. We design a neighbor selection module which enables each client to select a subset of its neighbors for the participation of model aggregation. We develop a decoder that enables each client to determine its transmit power and computational resource. Experimental results show that our proposed algorithm achieves a lower total delay and energy consumption on training when compared with five baseline schemes. Furthermore, by properly selecting a subset of neighbors for each client, our proposed algorithm achieves similar testing accuracy to the full participation scheme.