ArticlePatterns (New York, N.Y.)2024
A federated learning architecture for secure and private neuroimaging analysis.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Machine Learning and Multimodal Biomarker Discovery in Alzheimer's Disease.Brain sciences · 2026Review
- NeuroFLAME: A Scalable, Privacy-Preserving Federated Framework for Secure, Reproducible, and Multi-Site Neuroimaging Analysis.bioRxiv : the preprint server for biology · 2026Article
- The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).Neuroinformatics · 2026Review
- Artificial intelligence in hepatopancreatobiliary surgery for clinical outcome prediction: current perspective and future direction.Journal of robotic surgery · 2025Review
- A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference.Scientific reports · 2025Article
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration.Healthcare (Basel, Switzerland) · 2024Review
- A federated learning architecture for secure and private neuroimaging analysis.Patterns (New York, N.Y.) · 2024Article
- Privacy preservation for federated learning in health care.Patterns (New York, N.Y.) · 2024Review
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Secure federated learning for Alzheimer's disease detection.Frontiers in aging neuroscience · 2024Article
- The future of data analysis is now: Integrating generative AI in neuroimaging methods development.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Medical Imaging Applications of Federated Learning.Diagnostics (Basel, Switzerland) · 2023Review
- The pursuit of approaches to federate data to accelerate Alzheimer's disease and related dementia research: GAAIN, DPUK, and ADDI.Frontiers in neuroinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.