Evaluation of Federated learning variations for COVID-19 diagnosis using chest Radiographs from 42 US and European hospitals
Open accessAbstract
OBJECTIVE: Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations. MATERIALS AND METHODS: We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the Federated Averaging (FedAvg) algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, and FedAMP). RESULTS: We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, P = .5) and improved model generalizability with the FedAvg model (P < .05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation. CONCLUSION: FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.
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In our own experience, pre-processing of images is important and affects federated model performance.
“In our own experience, 53 we observed that pre-processing of images is important and affects the federated model performance.”
From AI pitfalls and what not to do: mitigating bias in AI (2023) · cites this paper as [53]
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References 8 · Cited by 36
Research works printed but not linked: 3
- [17]PyTorch: An Imperative Style, High-Performance Deep Learning Library. https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html. Accessed on April 14, 2022.
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- Authors
- Peng, L., Luo, G., Walker, A., Zaiman, Z., Jones, E. K., Gupta, H., Kersten, K., Burns, J. L., Harle, C. A., Magoc, T., Shickel, B., Steenburg, S. D., Loftus, T., Melton, G. B., Gichoya, J. W., Sun, J., & Tignanelli, C. J.
- Affiliations
- University of Minnesota (US)
- Emory University (US)
- Fairview Health Services (US)
- Nvidia (United States) (US)
- Indiana University Indianapolis (US)
- University of Florida Health (US)
- University of Florida College of Medicine , Gainesville, Florida, USA
- Corresponding author
- Tignanelli, Christopher J
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-10-10
- Volume
- 30
- Issue
- 1
- Pages
- 54-63
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC9619688
- OpenAlex
- W4304116665
- Cited by
- 36
- References
- 8
- Citations by year
- 2026: 12 · 2025: 8 · 2024: 6 · 2023: 10
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- IUScholarWorks (Indiana University) (submittedVersion)
- Funders
- Agency for Healthcare Research and Quality
- Patient-Centered Outcomes Research Institute (K12HS026379)
- National Institutes of Health’s
- National Center for Advancing Translational Sciences (KL2TR002492, UL1TR002494)
- The University of Minnesota Office of the Vice President of Research
- National Institute of Biomedical Imaging and Bioengineering
- MIDRC (75N92020C00008, 75N92020C00021)
- National Science Foundation (#1928481)
- Division of Electrical, Communication & Cyber Systems
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, COVID-19 diagnosis, chest radiograph, data heterogeneity, personalized federated learning, FedAvg, FedProx, model generalization, internal validation, external validation
- MeSH terms
- COVID-19, COVID-19 Testing, Europe, Hospitals, Humans, Learning, United States
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