Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
Open accessAbstract
Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems. Our code is publicly available at: https://github.com/xxlya/Fed_ABIDE/.
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In histopathology, Lu et al. reported DP guarantees for a neural network classifier trained with federated learning, following Li et al.
“In histopathology, Lu et al. 13 reported DP guarantees for a neural network classifier trained with FL, following Li et al. 26 .”
From Federated Learning and Differential Privacy for Medical Image Analysis (2021) · cites this paper as [26]
Using partial weight updates during federated learning, models can be trained to reach performance comparable to training while sharing full information.
“We show that by using partial weight updates during FL, models can be trained that reach a performance comparable to training while sharing the full information.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [52]
Most existing FL research in medical image analysis uses databases from different medical sites/centers for simulation and experiment.
“The first way is to directly use databases from different medical sites/centers [48,100].”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [48]
Li et al. [20] applied deep learning in a FL environment to classify MRI images for identifying ASD.
“Li et al. [20] applied deep learning in a FL environment to classify MRI images.Their model worked for identifying the ASD using the MRI analysis technique.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
Li et al. [20] and Linardos et al. [26] both used MRI images for their models, brain and heart MRI data respectively.
“In addition, Li et al. [20] and Linardos et al. [26] both used MRI images for their models, brain and heart MRI data consequently.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
Four reviewed papers [16], [18], [20], [26] used very small amounts of data: 153, 216, 370, and 180 samples respectively.
“As Table 5 shows, four papers [16], [18], [20] and [26] used very small amounts of data, their number of elements are 153, 216, 370, and 180 respectively.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
Li et al. [20] used a multi-layer perceptron (MLP) classifier, a deep neural network with one input, hidden, and output layer.
“Li et al. [20] have used multi-layer perceptron (MLP) classifier which was a deep neural network constructed by one input, hidden, and output layers.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
[20] and [27] used two different federated algorithms named Fed and secure aggregation (SecAgg), respectively.
“Likewise, [20] and [27] used two different federated algorithms named Fed, secure aggregation (SecAgg) respectively.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
Li et al. [20] used two DP mechanisms, Gaussian and Laplace, with noise level α varying from 0.001 to 1.
“Li et al. [20] used two different mechanisms of DP, Gaussian and Laplace.They defined the noise level α which varied from 0.001 to 1.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
The remaining three articles performed multi-class classification: Chakravarty et al. [23] used 14 classes, Połap et al. [17] seven classes, and Li et al. [20] four classes; their performances were AUC 80%, accuracy 70%, and 76%, respectively.
“Low: The rest three articles performed multi-class classification, where Chakravarty et al. [23] 14, Połap et al. [17] seven, and Li et al. [20] have considered four classes, their acquired performances are AUC 80%, accuracy 70% and 76% respectively.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
The highest quality score was 9 out of 12, gained by [27], followed by six for [20] and [28] individually.
“Highest score is 9 out of 12 gained by [27], followed by six for [20] and [28] both articles individually.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
In Li et al. [20], all segments of the experiment are well presented.
“All segments of the experiment are well”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
Li et al. [20] provided source code online.
“provided source code online”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
In Li et al. [20], the number of data samples is very small.
“Number of data samples is very”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
In Li et al. [20], non-IID data is not considered.
“non-IID data is not consid-”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
In Li et al. [20], an additional privacy method is used.
“additional privacy method is used.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [20]
By collectively building a global model for the prediction of brain age, the FL paradigm in the neuroimaging domain works effectively.
“By collectively building a global model for the prediction of brain age, the FL paradigm in the neuroimaging domain works effectively.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [107]
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- Authors
- Li, X., Gu, Y., Dvornek, N., Staib, L. H., Ventola, P., & Duncan, J. S.
- Affiliations
- Yale University (US)
- Zhejiang University (CN)
- Corresponding author
- Li, Xiaoxiao
- Duncan, James S.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 65
- Pages
- 101765
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC7569477
- OpenAlex
- W3040685212
- Cited by
- 480
- References
- 68
- Citations by year
- 2026: 68 · 2025: 85 · 2024: 88 · 2023: 96 · 2022: 76 · 2021: 54 · 2020: 12 · 2019: 1
- FWCI
- 23.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Image Analysis (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, privacy-preserving machine learning, domain adaptation, ABIDE dataset, neuroimaging biomarkers, gradient privacy
- MeSH terms
- Algorithms, Humans, Magnetic Resonance Imaging, Databases, Factual, Privacy
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