跨站点脑影像分析中，用联邦学习建模差异数据，不共享原始数据却提升模型效果。
Personalized Federated Dictionary Learning for Modeling Heterogeneity in Multi-site fMRI Data
- 各站点独立学字典，拆分为共用全局部分和本地个性化部分。
- 全局字典联邦聚合，本地字典独立优化，兼顾一致性与特异性。
- 在ABIDE数据集上比现有方法更准确、更鲁棒，适合多中心研究。
数据隐私限制给大规模神经影像分析带来挑战，尤其在多站点功能磁共振成像（fMRI）研究中，站点间异质性导致数据非独立同分布（non-IID），阻碍可泛化模型的构建。为此，我们提出个性化联邦字典学习（PFedDL），一种新型联邦学习框架，可在不共享原始数据的前提下实现跨站点协作建模。PFedDL在各站点独立进行字典学习，将每个站点的字典分解为共享的全局成分和个性化的本地成分。全局原子通过联邦聚合更新以增强跨站点一致性，而本地原子则独立优化以捕捉站点特异性变异，从而提升下游分析性能。在ABIDE数据集上的实验表明，PFedDL在非IID数据集上优于现有方法，在准确性和鲁棒性方面均有提升。
原文摘要 · Abstract (English)
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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