Federated asynchronous graph attention network with structural semantic embedding for multi-label graph...

Area of Science:

• Artificial Intelligence
• Machine Learning
• Network Science

Background:

• Federated Learning (FL) enables privacy-preserving training of Graph Neural Networks (GNNs).
• Traditional FL-GNNs often neglect label semantics and struggle with data heterogeneity across clients.
• Client-side graph representation inconsistencies and distributed graph variations hinder FL-GNN performance.

Purpose of the Study:

• To introduce a novel FL-GNN framework, FasSGAT, that addresses label semantic and graph heterogeneity for multi-label classification.
• To enhance FL-GNN performance by incorporating label semantics and mitigating intra-client and inter-client heterogeneity.
• To develop a structure-sensitive asynchronous aggregation mechanism for robust global model construction.

Main Methods:

• Developed client-specific label semantic embedding modules using label-semantic distribution graphs.
• Integrated label embeddings and structure-sensitive spectral features into a multi-label classifier to address client-side heterogeneity.
• Implemented a novel server-level structure-sensitive asynchronous aggregation mechanism utilizing graph spectral features.

Main Results:

• FasSGAT effectively learns feature encodings from label-semantic distribution graphs.
• The model successfully mitigates client-side heterogeneity using specialized spectral features.
• Experimental results demonstrate FasSGAT's superior performance over traditional FL methods on multi-label benchmarks.

Conclusions:

• FasSGAT offers a significant advancement in federated learning for GNNs, particularly in multi-label classification.
• The framework successfully addresses the critical challenges of label semantic and graph heterogeneity.
• The proposed methods enhance model performance and robustness in privacy-sensitive, distributed graph learning scenarios.