Cloud-based federated learning implementation across medical centers
Open accessAbstract
PURPOSE: Building well-performing machine learning (ML) models in health care has always been exigent because of the data-sharing concerns, yet ML approaches often require larger training samples than is afforded by one institution. This paper explores several federated learning implementations by applying them in both a simulated environment and an actual implementation using electronic health record data from two academic medical centers on a Microsoft Azure Cloud Databricks platform. MATERIALS AND METHODS: Using two separate cloud tenants, ML models were created, trained, and exchanged from one institution to another via a GitHub repository. Federated learning processes were applied to both artificial neural networks (ANNs) and logistic regression (LR) models on the horizontal data sets that are varying in count and availability. Incremental and cyclic federated learning models have been tested in simulation and real environments. RESULTS: < .05). Single weight neural network models showed improvement in some cases. However, LR models did not show much improvement after federated learning processes. The specific process that improved the performance differed based on the ML model and how federated learning was implemented. Moreover, we have confirmed that the order of the institutions during the training did influence the overall performance increase. CONCLUSION: Unlike previous studies, our work has shown the implementation and effectiveness of federated learning processes beyond simulation. Additionally, we have identified different federated learning models that have achieved statistically significant performances. More work is needed to achieve effective federated learning processes in biomedicine, while preserving the security and privacy of the data.
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Some recent surveys on federated learning in medicine and healthcare focus on electronic health records or internet of medical things without paying attention to medical imaging.
“However, some of them focus on electronic health records [21,22] or internet of medical things [26], without paying attention to medical imaging.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [22]
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Federated learning for medical image analysis: A survey
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- Authors
- Rajendran, S., Obeid, J. S., Binol, H., D`Agostino, R., Foley, K., Zhang, W., Austin, P., Brakefield, J., Gurcan, M. N., & Topaloglu, U.
- Affiliations
- Georgia Institute of Technology (US)
- Medical University of South Carolina (US)
- Wake Forest University Health Sciences (US)
- Databricks (United States) (US)
- Microsoft (United States) (US)
- Center for Biomedical Informatics, Wake Forest University School of Medicine, Winston Salem, NC
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston Salem, NC
- Venue
- JCO Clinical Cancer Informatics
- Published
- 2021-12
- Volume
- 5
- Issue
- 5
- Pages
- 1-11
- ISSN
- 2473-4276 (ISSN-L)
- PMC
- PMC8140794
- OpenAlex
- W3120801295
- Cited by
- 76
- References
- 22
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 21 · 2023: 15 · 2022: 10 · 2021: 8
- FWCI
- 4.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- JCO Clinical Cancer Informatics (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR001420, UL1TR001450, CA012197)
- National Cancer Institute (P30CA012197)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK123704)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, cloud computing, electronic health records, data privacy, artificial neural networks, logistic regression, model performance
- MeSH terms
- Cloud Computing, Machine Learning, Humans, Neural Networks, Computer, Privacy, Information Dissemination
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