This research introduces a novel approach combining Graph Neural Networks with Federated Learning to address privacy challenges in graph data analysis.

• Enables collaborative training without centralizing sensitive graph data
• Preserves data privacy while maintaining learning effectiveness
• Specifically designed to handle non-IID (non-independent and identically distributed) graph data
• Integrates spectral graph transformers with neural ordinary differential equations

This innovation is significant for security applications as it allows organizations to benefit from advanced graph analytics while complying with privacy regulations and protecting sensitive network information.

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