FGDN: A Federated Graph Convolutional Network framework for multi-site major depression disorder diagnosis.

The vast amount of healthcare data is characterized by its diversity, dynamic nature, and large scale. It is a challenge that directly training a Graph Convolutional Neural Network (GCN) in a multi-site dataset poses to protecting the privacy of Major Depressive Disorder (MDD) patients. Federated learning enables the training of a global model without the need to share data. However, some previous methods overlook the potential value of non-image information, such as gender, age, education years, and site information. Multi-site datasets often exhibit the problem of Non-Independent and Identically Distributed (Non-IID) data, which leads to the loss of edge information across local models, ultimately weakening the generalization ability of the federated learning models. Accordingly, we propose a Federated Graph Convolutional Network framework with Dual Graph Attention Network (FGDN) for multi-site MDD diagnosis. Specifically, both linear and nonlinear information are extracted from the functional connectivity matrix via different correlation measures. A Dual Graph Attention Network (DGAT) module is designed to capture complementary information between these two types. Then a Federated Graph Convolutional Network (FedGCN) module is introduced to address the issue of missing edge information across local models. It allows each local model to receive aggregated feature information from neighboring nodes of other local models. Additionally, the privacy of patients is protected with fully homomorphic encryption. The experimental results demonstrate that FGDN achieves a classification accuracy of 61.8% on 841 subjects from three different sites, and outperforms some recent centralized learning frameworks and federated learning frameworks. This proves it fully mines the feature information in brain functional connectivity, alleviates the information loss caused by Non-IID data, and secures the healthcare data.