Fully Distributed Online Training of Graph Neural Networks in Networked Systems

Published in IEEE ICMLCN 2025, 2024

Recommended citation: Rostyslav Olshevskyi, Zhongyuan Zhao, Kevin Chan, Gunjan Verma, Ananthram Swami, and Santiago Segarra, " Fully Distributed Online Training of Graph Neural Networks in Networked Systems," IEEE International Conference on Machine Learning for Communicatio and Networking (ICMLCN) 2025, under review

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Abstract

Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power grids, and transportation networks. Currently, GNNs in networked systems mostly follow a paradigm of ‘centralized training, distributed execution’, which limits their adaptability and slows down their development cycles. In this work, we fill this gap for the first time by developing a communication-efficient, fully distributed online training approach for GNNs applied to large networked systems. For a mini-batch with $B$ samples, our approach of training an $L$-layer GNN only adds $L$ rounds of message passing to the $LB$ rounds required by GNN inference, with doubled message sizes. Through numerical experiments in graph-based node regression, power allocation, and link scheduling in wireless networks, we demonstrate the effectiveness of our approach in training GNNs under supervised, unsupervised, and reinforcement learning paradigms.