ArticleFrontiers in aging neuroscience2024
Secure federated learning for Alzheimer's disease detection.
Article in Frontiers in aging neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Stage-Wise IoT Solutions for Alzheimer's Disease: A Systematic Review of Detection, Monitoring, and Assistive Technologies.Sensors (Basel, Switzerland) · 2025Pooled it
- Multimodal Deep Learning Approaches for Early Detection of Alzheimer's Disease: A Comprehensive Systematic Review of Image Processing Techniques.Current Alzheimer research · 2025Pooled it
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Skin disease diagnostics through federated transfer learning on heterogeneous data.Scientific reports · 2026Article
- FuzzyFed-CNN: secure and explainable multimodal federated learning for early Alzheimer's diagnosis.Frontiers in artificial intelligence · 2026Article
- A Scoping Review of Machine Learning-Based Prediction of Alzheimer's Disease Using Blood Biomarkers.Biomedical engineering and computational biology · 2026Review
- Federated Deep Learning Approaches for Detecting Ocular Diseases in Medical Imaging: A Systematic Review.Current medical imaging · 2025Article
- Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.Frontiers in medicine · 2025Article
- An Evolutionary Federated Learning Approach to Diagnose Alzheimer's Disease Under Uncertainty.Diagnostics (Basel, Switzerland) · 2025Article
- A federated learning architecture for secure and private neuroimaging analysis.Patterns (New York, N.Y.) · 2024Article
- Harmonizing AI governance regulations and neuroinformatics: perspectives on privacy and data sharing.Frontiers in neuroinformatics · 2024Article
- Benchmarking privacy and utility in synthetic tabular cohorts for Alzheimer's disease research.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine Learning (ML) is considered a promising tool to aid and accelerate diagnosis in various medical areas, including neuroimaging. However, its success is set back by the lack of large-scale public datasets. Indeed, medical institutions possess a large amount of data; however, open-sourcing is prevented by the legal requirements to protect the patient's privacy. Federated Learning (FL) is a viable alternative that can overcome this issue. This work proposes training an ML model for Alzheimer's Disease (AD) detection based on structural MRI (sMRI) data in a federated setting. We implement two aggregation algorithms, Federated Averaging (FedAvg) and Secure Aggregation (SecAgg), and compare their performance with the centralized ML model training. We simulate heterogeneous environments and explore the impact of demographical (sex, age, and diagnosis) and imbalanced data distributions. The simulated heterogeneous environments allow us to observe these statistical differences' effect on the ML models trained using FL and highlight the importance of studying such differences when training ML models for AD detection. Moreover, as part of the evaluation, we demonstrate the increased privacy guarantees of FL with SecAgg via simulated membership inference attacks.
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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.