Preserving Specificity in Federated Graph Learning for fMRI-based
  Neurological Disorder Identification
Junhao Zhang Qianqian Wang Xiaochuan Wang Lishan Qiao Mingxia Liu
Resting-state functional magnetic resonance imaging (rs-fMRI) offers a
non-invasive approach to examining abnormal brain connectivity associated with
brain disorders. Graph neural network (GNN) gains popularity in fMRI
representation learning and brain disorder analysis with powerful graph
representation capabilities. Training a general GNN often necessitates a
large-scale dataset from multiple imaging centers/sites, but centralizing
multi-site data generally faces inherent challenges related to data privacy,
security, and storage burden. Federated Learning (FL) enables collaborative
model training without centralized multi-site fMRI data. Unfortunately,
previous FL approaches for fMRI analysis often ignore site-specificity,
including demographic factors such as age, gender, and education level. To this
end, we propose a specificity-aware federated graph learning (SFGL) framework
for rs-fMRI analysis and automated brain disorder identification, with a server
and multiple clients/sites for federated model aggregation and prediction. At
each client, our model consists of a shared and a personalized branch, where
parameters of the shared branch are sent to the server while those of the
personalized branch remain local. This can facilitate knowledge sharing among
sites and also helps preserve site specificity. In the shared branch, we employ
a spatio-temporal attention graph isomorphism network to learn dynamic fMRI
representations. In the personalized branch, we integrate vectorized
demographic information (i.e., age, gender, and education years) and functional
connectivity networks to preserve site-specific characteristics.
Representations generated by the two branches are then fused for
classification. Experimental results on two fMRI datasets with a total of 1,218
subjects suggest that SFGL outperforms several state-of-the-art approaches.