Federated Graph Neural Networks: Overview, Techniques and Challenges

TL;DR

This survey comprehensively reviews federated graph neural networks (FedGNNs), highlighting their techniques, challenges, and future directions in integrating GNNs with federated learning for privacy-preserving graph data analysis.

Contribution

It provides the first detailed taxonomy and analysis of FedGNNs, addressing key challenges and outlining future research opportunities in this emerging field.

Findings

Taxonomy of FedGNNs based on GNN and FL integration

Analysis of heterogeneity handling in FedGNNs

Discussion of challenges and future research directions

Abstract

With its capability to deal with graph data, which is widely found in practical applications, graph neural networks (GNNs) have attracted significant research attention in recent years. As societies become increasingly concerned with the need for data privacy protection, GNNs face the need to adapt to this new normal. Besides, as clients in Federated Learning (FL) may have relationships, more powerful tools are required to utilize such implicit information to boost performance. This has led to the rapid development of the emerging research field of federated graph neural networks (FedGNNs). This promising interdisciplinary field is highly challenging for interested researchers to grasp. The lack of an insightful survey on this topic further exacerbates the entry difficulty. In this paper, we bridge this gap by offering a comprehensive survey of this emerging field. We propose a 2-dimensional taxonomy of the FedGNNs literature: 1) the main taxonomy provides a clear perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL training as well as how FL assists GNNs training, and 2) the auxiliary taxonomy provides a view on how FedGNNs deal with heterogeneity across FL clients. Through discussions of key ideas, challenges, and limitations of existing works, we envision future research directions that can help build more robust, explainable, efficient, fair, inductive, and comprehensive FedGNNs.