Graph Neural Networks via AirComp: Algorithms and Performance Analysis

Kang Wei, Guangji Chen, Yan Hong, Long Shi, Feng Shu

Abstract: Graph neural networks (GNNs) over the air (AirGNNs) has shown significant potential for efficiently training distributed GNN models by aggregating the feature representations based on the over the air computation (AirComp) technique. However, devices in existing wireless systems operate in half-duplex mode, the collision issue that devices cannot transmit and receive simultaneously may limit the potential of AirGNNs. To tackle this challenge, we proposed two scheduling methods, i.e., collision-aware graph division (CAGD) and interference-aware graph division (IAGD), which can achieve collision avoidance and interference control, respectively. Furthermore, we derive the convergence bound of AirGNNs that reveals the following three key properties: 1) the convergence bound is a function of the accumulated transmission errors of all devices; 2) there is an optimal transmit power for each device in terms of training performance; and 3) there is a fundamental tradeoff between training performance and transmission efficiency. Extensive experimental results on real-world datasets validate the effectiveness of our proposed methods and insights in the theoretical results.

External IDs:dblp:journals/wcl/WeiCHSS26