ASFGNN: Automated Separated-Federated Graph Neural Network

TL;DR

This paper introduces ASFGNN, an automated federated GNN framework that addresses data non-IID issues and hyper-parameter tuning challenges, improving accuracy and efficiency in federated learning scenarios.

Contribution

The paper proposes a separated-federated GNN model and uses Bayesian optimization for automatic hyper-parameter tuning, enhancing federated GNN performance and efficiency.

Findings

ASFGNN outperforms naive federated GNN in accuracy.

ASFGNN significantly improves hyper-parameter tuning efficiency.

Experiments on benchmark datasets validate the effectiveness of ASFGNN.

Abstract

Graph Neural Networks (GNNs) have achieved remarkable performance by taking advantage of graph data. The success of GNN models always depends on rich features and adjacent relationships. However, in practice, such data are usually isolated by different data owners (clients) and thus are likely to be Non-Independent and Identically Distributed (Non-IID). Meanwhile, considering the limited network status of data owners, hyper-parameters optimization for collaborative learning approaches is time-consuming in data isolation scenarios. To address these problems, we propose an Automated Separated-Federated Graph Neural Network (ASFGNN) learning paradigm. ASFGNN consists of two main components, i.e., the training of GNN and the tuning of hyper-parameters. Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally. To handle the time-consuming parameter tuning problem, we leverage Bayesian optimization technique to automatically tune the hyper-parameters of all the clients. We conduct experiments on benchmark datasets and the results demonstrate that ASFGNN significantly outperforms the naive federated GNN, in terms of both accuracy and parameter-tuning efficiency.