# Distributed Training of Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2007.06281 · HTML: https://mlsanity.com/paper/2007.06281/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 14 of 14)

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- [Graph Filters for Signal Processing and Machine Learning on Graphs](https://mlsanity.com/paper/2211.08854/) (2022)
- [Federated Graph Neural Networks: Overview, Techniques and Challenges](https://mlsanity.com/paper/2202.07256/) (2022)
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- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://mlsanity.com/paper/2210.17281/) (2022)
- [Gradient Inversion Attack on Graph Neural Networks](https://mlsanity.com/paper/2411.19440/) (2024)
- [DeceFL: A Principled Decentralized Federated Learning Framework](https://mlsanity.com/paper/2107.07171/) (2021)
- [Distributed Optimization of Graph Convolutional Network using Subgraph Variance](https://mlsanity.com/paper/2110.02987/) (2021)
- [Tackling the Local Bias in Federated Graph Learning](https://mlsanity.com/paper/2110.12906/) (2021)
- [One Node Per User: Node-Level Federated Learning for Graph Neural Networks](https://mlsanity.com/paper/2409.19513/) (2024)
- [SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation](https://mlsanity.com/paper/2407.11085/) (2024)
- [FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks](https://mlsanity.com/paper/2412.16144/) (2024)
- [PassREfinder-FL: Privacy-Preserving Credential Stuffing Risk Prediction via Graph-Based Federated Learning for Representing Password Reuse between Websites](https://mlsanity.com/paper/2510.16083/) (2025)
- [Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks](https://mlsanity.com/paper/2605.26243/) (2026)

## Abstract (arXiv)

The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs). The proposed method is able to exploit the meaningful relational structure of the input data, which are collected by a set of agents that communicate over a sparse network topology. After formulating the centralized GCN training problem, we first show how to make inference in a distributed scenario where the underlying data graph is split among different agents. Then, we propose a distributed gradient descent procedure to solve the GCN training problem. The resulting model distributes computation along three lines: during inference, during back-propagation, and during optimization. Convergence to stationary solutions of the GCN training problem is also established under mild conditions. Finally, we propose an optimization criterion to design the communication topology between agents in order to match with the graph describing data relationships. A wide set of numerical results validate our proposal. To the best of our knowledge, this is the first work combining graph convolutional neural networks with distributed optimization.