  Graph Convolutional Neural Networks (GCNs) are widely used for graph
analysis. Specifically, in medical applications, GCNs can be used for disease
prediction on a population graph, where graph nodes represent individuals and
edges represent individual similarities. However, GCNs rely on a vast amount of
data, which is challenging to collect for a single medical institution. In
addition, a critical challenge that most medical institutions continue to face
is addressing disease prediction in isolation with incomplete data information.
To address these issues, Federated Learning (FL) allows isolated local
institutions to collaboratively train a global model without data sharing. In
this work, we propose a framework, FedNI, to leverage network inpainting and
inter-institutional data via FL. Specifically, we first federatively train
missing node and edge predictor using a graph generative adversarial network
(GAN) to complete the missing information of local networks. Then we train a
global GCN node classifier across institutions using a federated graph learning
platform. The novel design enables us to build more accurate machine learning
models by leveraging federated learning and also graph learning approaches. We
demonstrate that our federated model outperforms local and baseline FL methods
with significant margins on two public neuroimaging datasets.
