Federated Linear Mixed Effects Modeling for Voxel-Based Morphometry

We propose a federated linear mixed-effects (LME) model to perform a large-scale analysis of data gathered from different collaborations without the need to pool or share the actual data. This method is efficient as it overcomes the hurdles of data privacy for sharing and has lower bandwidth and mem...

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Bibliographic Details
Published in2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) pp. 1 - 4
Main Authors Basodi, Sunitha, Raja, Rajikha, Gazula, Harshvardhan, Romero, Javier Tomas, Panta, Sandeep, Calhoun, Vince D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.04.2023
Subjects
Analytical models
Biological system modeling
COINSTAC
Collaboration
Data privacy
Federated learning
Linear mixed-effects (LME)
Magnetic resonance imaging
Memory management
Neuroimaging
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Summary:We propose a federated linear mixed-effects (LME) model to perform a large-scale analysis of data gathered from different collaborations without the need to pool or share the actual data. This method is efficient as it overcomes the hurdles of data privacy for sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. Our decentralized LME approach is optimized for neuroimaging data. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results demonstrate that federated (decentralized) LME models achieve similar performance compared to the models trained with all the data in one location.
ISSN:1945-8452
DOI:10.1109/ISBI53787.2023.10230684