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

## TL;DR

This paper introduces FasSGAT, a new federated learning method that improves multi-label graph classification by incorporating label semantics and handling graph heterogeneity.

## Contribution

The novel FasSGAT framework integrates label semantic embeddings and structure-sensitive aggregation to address graph heterogeneity in federated learning.

## Key findings

- FasSGAT outperforms traditional FL methods on multi-label graph classification benchmarks.
- The integration of label semantic embeddings improves model performance in heterogeneous settings.
- Structure-sensitive aggregation effectively addresses inter-client graph heterogeneity.

## Abstract

Federated Learning (FL) provides a privacy-preserving framework for training graph neural networks (GNNs) in privacy-sensitive scenarios. However, traditional FL-GNN approaches often focus on addressing data distribution inconsistencies across clients from a purely data-centric viewpoint, overlooking the critical role of label semantics. Incorporating label semantic information, however, significantly enhances a model’s performance in multi-label classification tasks. Moreover, the performance of FL-GNNs is constrained by two key challenges related to heterogeneity: intra-client heterogeneity in graph representations and inter-client heterogeneity across distributed graphs. Unfortunately, few FL methods effectively handle discrepancies in both label distributions and graph heterogeneity across different clients. To address this gap, we introduce the federated asynchronous graph attention network with structural semantic embedding for multi-label classification (FasSGAT). FasSGAT primarily contains: (1) client-specific label semantic embedding modules that learn feature encodings from constructed label-semantic distribution graphs, (2) the integration of these encodings into the backbone multi-label classifier, along with specially designed structure-sensitive spectral features to mitigate client-side heterogeneity, and (3) a novel structure-sensitive asynchronous aggregation mechanism at the server level that uses the graph spectral features to construct a global model and address graph heterogeneity. Our experimental results on multi-label benchmarks show that FasSGAT outperforms traditional FL methods across various evaluation metrics.