In large-scale neuroimaging initiatives like PPMI and ADNI, data is collected across multiple sites, but direct pooling is often limited, or entirely hindered, by local IRB restrictions, regulatory constraints, and site-specific data sharing policies. Additionally, many sites lack the computing infrastructure or technical expertise to host or train centralized models on large-scale MRI/PET datasets.
Federated learning (FL) offers a powerful alternative: instead of moving sensitive imaging and clinical data, the model is sent to the data. Each site runs the analysis locally and returns model updates (not raw data) to a central server for aggregation and downstream analysis, ensuring that no raw data ever leaves the site.
This approach enables:
- Cross-center collaboration without violating data governance or patient privacy laws
- Scalable model training, even with limited local computational resources
- Robustness to scanner and protocol variability across sites
- Discovery of generalizable neuroimaging biomarkers for PD, AD, and other neurodegenerative diseases
Toolkits like Flower, Fed-BioMed, and MONAI FL provide robust frameworks to deploy FL in realistic, multi-site research settings not just in neuroscience and biomedicine, but across other domains where privacy and decentralization are key.