Personalized Federated Dictionary Learning for Modeling Heterogeneity in Multi-site fMRI Data

• URL: http://arxiv.org/abs/2509.20627v1
• Date: Thu, 25 Sep 2025 00:01:02 GMT
• Title: Personalized Federated Dictionary Learning for Modeling Heterogeneity in Multi-site fMRI Data
• Authors: Yipu Zhang, Chengshuo Zhang, Ziyu Zhou, Gang Qu, Hao Zheng, Yuping Wang, Hui Shen, Hongwen Deng,
• Abstract summary: PFedDL performs independent dictionary learning at each site, decomposing each site-specific dictionary into a shared global component and a personalized local component.<n>Experiments on the ABIDE dataset demonstrate that PFedDL outperforms existing methods in accuracy and robustness across non-IID datasets.
• Score: 14.18552770292156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data privacy constraints pose significant challenges for large-scale neuroimaging analysis, especially in multi-site functional magnetic resonance imaging (fMRI) studies, where site-specific heterogeneity leads to non-independent and identically distributed (non-IID) data. These factors hinder the development of generalizable models. To address these challenges, we propose Personalized Federated Dictionary Learning (PFedDL), a novel federated learning framework that enables collaborative modeling across sites without sharing raw data. PFedDL performs independent dictionary learning at each site, decomposing each site-specific dictionary into a shared global component and a personalized local component. The global atoms are updated via federated aggregation to promote cross-site consistency, while the local atoms are refined independently to capture site-specific variability, thereby enhancing downstream analysis. Experiments on the ABIDE dataset demonstrate that PFedDL outperforms existing methods in accuracy and robustness across non-IID datasets.

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