Federated learning in a medical context: A systematic literature review
Open accessAbstract
Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.
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Federated models have increased generalizability, ongoing improvement from far-off data sources, and adherence to data security rules like HIPAA and GDPR.
“Increased generalizability, ongoing improvement from far-off data sources, and adherence to data security rules like HIPAA and GDPR are features of federated models [183].”
From Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease (2025) · cites this paper as [183]
Some survey papers cover very broad areas in medicine and healthcare applications without detailed introduction on federated learning in medical image analysis.
“And some survey papers cover very broad areas in medicine and healthcare applications [23,24], without detailed introduction on federated learning in medical image analysis.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [24]
Existing systematic literature reviews on FL in healthcare covered overall healthcare applications rather than specifically medical image analysis.
“Meanwhile, some SLR have been published on the topic, however, they were about overall healthcare applications not particularly for the medical image analysis context.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [5]
A SLR [5] considered all articles that used all forms of medical data to train FL models.
“A SLR has been presented in [5], they considered all of the articles which have used all forms of medical data to train their FL models.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [5]
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Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease
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“Increased generalizability, ongoing improvement from far-off data sources, and adherence to data security rules like HIPAA and GDPR are features of federated models [183].”
- Authors
- Pfitzner, B., Steckhan, N., & Arnrich, B.
- Affiliations
- University of Potsdam (DE)
- University of Potsdam, Potsdam, Germany
- Venue
- ACM Transactions on Internet Technology
- Published
- 2021-06-02
- Volume
- 21
- Issue
- 2
- Pages
- 1-31
- ISSN
- 1533-5399 (ISSN-L)
- 1557-6051
- OpenAlex
- W3169231731
- Cited by
- 288
- References
- 49
- Citations by year
- 2026: 51 · 2025: 78 · 2024: 81 · 2023: 53 · 2022: 22 · 2021: 3
- FWCI
- 2.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Transactions on Internet Technology (publishedVersion, cc-by)
- publish.UP (University of Potsdam) (submittedVersion)
- Funders
- Federal Ministry of Education and Research of Germany in the framework of KI-LAB-ITSE (01IS19066)
- HPI Research School on Data Science and Engineering
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- federated learning, healthcare data privacy, patient anonymity, privacy-preserving machine learning, machine learning in medicine
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