Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs

Accepted at Transactions on Machine Learning Research (TMLR) 2025

Abstract

Graph Neural Network (GNN) research is rapidly advancing due to GNNs’ capacity to learn distributed representations from graph-structured data. However, centralizing large volumes of real-world graph data for GNN training is often impractical due to privacy concerns, regulatory restrictions, and commercial competition. Federated learning (FL), a distributed learning paradigm, offers a solution by preserving data privacy with collaborative model training. Despite progress in training huge vision and language models, federated learning for GNNs remains underexplored. To address this challenge, we present a novel method for federated learning on GNNs based on spectral GNNs equipped with neural ordinary differential equations (ODE) for better information capture, showing promising results across both homophilic and heterophilic graphs. Our approach effectively handles non-Independent and Identically Distributed (non-IID) data, while also achieving performance comparable to existing methods that only operate on IID data. It is designed to be privacy-preserving and bandwidth-optimized, making it suitable for real-world applications such as social network analysis, recommendation systems, and fraud detection, which often involve complex, non-IID, and heterophilic graph structures. Our results in the area of federated learning on non-IID heterophilic graphs demonstrate significant improvements, while also achieving better performance on homophilic graphs. This work highlights the potential of federated learning in diverse and challenging graph settings.

Paper

Link to the paper: https://openreview.net/

Getting Started

We use Poetry for dependency and environment management.

Installation

1. Install Poetry

curl -sSL https://install.python-poetry.org | python3 -

2. Clone the repository

https://github.com/SpringWiz11/Fed-GNODEFormer.git
cd Fed-GNODEFormer

3. Install dependencies using Poetry

poetry install

4. Activate the Poetry environment

poetry shell

Running Experiments

We support two execution modes:

➤ Federated Mode

Run the experiment in a federated learning setup:

python3 runner.py -m --mode federated

➤ Centralized Mode

Run the experiment in a centralized learning setup:

python3 runner.py -m --mode centralized

🤝 Contributing

We welcome contributions, issues, and discussions!
Please open an issue or submit a pull request if you have ideas to improve the project.