FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

TL;DR

FedGraphNN introduces an open federated learning benchmark system for graph neural networks, enabling research on privacy-preserving distributed GNN training across diverse datasets and models.

Contribution

It provides a comprehensive, unified platform with multiple datasets, models, and algorithms for federated GNN research, addressing a gap in existing FL tools.

Findings

Federated GNNs perform worse than centralized GNNs on most non-IID datasets.

The best centralized GNN model may not be optimal in federated settings.

FedGraphNN system is computationally efficient and secure for large-scale graph datasets.

Abstract

Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to privacy concerns, regulation restrictions, and commercial competitions. Federated learning (FL), a trending distributed learning paradigm, provides possibilities to solve this challenge while preserving data privacy. Despite recent advances in vision and language domains, there is no suitable platform for the FL of GNNs. To this end, we introduce FedGraphNN, an open FL benchmark system that can facilitate research on federated GNNs. FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support. Particularly for the datasets, we collect, preprocess, and partition 36 datasets from 7 domains, including both publicly available ones and specifically obtained ones such as hERG and Tencent. Our empirical analysis showcases the utility of our benchmark system, while exposing significant challenges in graph FL: federated GNNs perform worse in most datasets with a non-IID split than centralized GNNs; the GNN model that attains the best result in the centralized setting may not maintain its advantage in the FL setting. These results imply that more research efforts are needed to unravel the mystery behind federated GNNs. Moreover, our system performance analysis demonstrates that the FedGraphNN system is computationally efficient and secure to large-scale graphs datasets. We maintain the source code at https://github.com/FedML-AI/FedGraphNN.