Toward Integrating Federated Learning With Split Learning via Spatio-Temporal Graph Framework for Brain Disease Prediction

• Published in: IEEE transactions on medical imaging Vol. 44; no. 3; pp. 1334 - 1346
• Main Authors: Mao, Junbin, Liu, Jin, Tian, Xu, Pan, Yi, Trucco, Emanuele, Lin, Hanhe
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2025
• Subjects: Algorithms; Brain - diagnostic imaging; brain disease; Brain Diseases - diagnostic imaging; Brain modeling; Computational modeling; Data models; Data privacy; Diseases; Federated Learning; Functional magnetic resonance imaging; Humans; Image Interpretation, Computer-Assisted - methods; Machine Learning; Magnetic Resonance Imaging - methods; Predictive models; Servers; Spatio-temporal graph network; split learning; Training
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2024.3493195

Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, collecting and labeling the data is time-consuming and costly, which limits the amount of valid data collected at a single healthcare site; on the other hand, integrating data from multiple sites is challenging due to data privacy restrictions. To address these issues, we propose a novel, integrated Federated learning and Split learning Spatio-temporal Graph framework (F<inline-formula> <tex-math notation="LaTeX">\text {S}^{{2}} </tex-math></inline-formula>G). Specifically, we introduce federated learning and split learning techniques to split a spatio-temporal model into a client temporal model and a server spatial model. In the client temporal model, we propose a time-aware mechanism to focus on changes in brain functional states and use an InceptionTime model to extract information about changes in the brain states of each subject. In the server spatial model, we propose a united graph convolutional network to integrate multiple graph convolutional networks. Integrating federated learning and split learning, F<inline-formula> <tex-math notation="LaTeX">\text {S}^{{2}} </tex-math></inline-formula>G can utilize multi-site fMRI data without violating data privacy protection and reduce the risk of overfitting as it is capable of learning from limited training data sets. Moreover, it boosts the extraction of spatio-temporal features of fMRI using spatio-temporal graph networks. Experiments on ABIDE and ADHD200 datasets demonstrate that our proposed method outperforms state-of-the-art methods. In addition, we explore biomarkers associated with brain disease prediction using community discovery algorithms using intermediate results of F<inline-formula> <tex-math notation="LaTeX">\text {S}^{{2}} </tex-math></inline-formula>G. The source code is available at https://github.com/yutian0315/FS2G .