A federated learning architecture for secure and private neuroimaging analysis
Open accessAbstract
The amount of biomedical data continues to grow rapidly. However, collecting data from multiple sites for joint analysis remains challenging due to security, privacy, and regulatory concerns. To overcome this challenge, we use federated learning, which enables distributed training of neural network models over multiple data sources without sharing data. Each site trains the neural network over its private data for some time and then shares the neural network parameters (i.e., weights and/or gradients) with a federation controller, which in turn aggregates the local models and sends the resulting community model back to each site, and the process repeats. Our federated learning architecture, MetisFL, provides strong security and privacy. First, sample data never leave a site. Second, neural network parameters are encrypted before transmission and the global neural model is computed under fully homomorphic encryption. Finally, we use information-theoretic methods to limit information leakage from the neural model to prevent a "curious" site from performing model inversion or membership attacks. We present a thorough evaluation of the performance of secure, private federated learning in neuroimaging tasks, including for predicting Alzheimer's disease and for brain age gap estimation (BrainAGE) from magnetic resonance imaging (MRI) studies in challenging, heterogeneous federated environments where sites have different amounts of data and statistical distributions.
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In a particular study, attackers could rebuild images of MRI scans from gradients shared in a federated learning configuration, highlighting the privacy risks presented by unprotected gradient sharing in healthcare systems.
“In a particular study, attackers could rebuild images of MRI scans from gradients shared in an FL configuration, highlighting the privacy risks presented by unprotected gradient sharing in healthcare systems [80].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [80]
The Experimental procedures section (page unavailable) says gradients can leak information about individual samples, supporting the general privacy-risk point. But the paper does not report attackers reconstructing MRI images from shared gradients. Its Results section instead reports successful membership-inference attacks and says model inversion against the global federated model is impractical in realistic settings. (LLM, conf=0.85)
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- [29]Stripelis D. Gupta U. Saleem H. Dhinagar N. Ghai T. Anastasiou C. Sanchez R. Steeg G.V. Ravi S. Naveed M. Metisfl 2024 University of Southern California. Zenodo 10.5281/zenodo.11411754 PMC11368680 39233693 · DOI
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- Authors
- Stripelis, D., Gupta, U., Saleem, H., Dhinagar, N., Ghai, T., Anastasiou, C., Sánchez, R., Steeg, G. V., Ravi, S., Naveed, M., Thompson, P. M., & Ambite, J. L.
- Affiliations
- University of Southern California (US)
- University of California, Riverside (US)
- Corresponding author
- Stripelis, Dimitris
- ORCID
- Venue
- Patterns
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 5
- Issue
- 8
- Pages
- 101031
- ISSN
- 2666-3899 (ISSN-L)
- PMC
- PMC11368680
- OpenAlex
- W4401215961
- Cited by
- 31
- References
- 125
- Citations by year
- 2026: 16 · 2025: 12 · 2024: 3
- FWCI
- 4.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Patterns (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- federated learning, neuroimaging data analysis, privacy-preserving machine learning, fully homomorphic encryption, information leakage, model inversion attacks, membership inference attacks, Alzheimer's disease prediction, magnetic resonance imaging, heterogeneous federated learning, data security
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