Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification

We advance privacy-preserving, ML-based DR screening technology, supporting the goal of equitable vision care worldwide.

Authors

Institutions

Publication Details

Journal
Journal of Medical Imaging
Published
2025-06-02
DOI
https://doi.org/10.1117/1.jmi.12.3.034504
Citations
3
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
4.08
Controls
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article

Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification

Nils D. Forkert, Christopher D. Nielsen, Matthias Wilms
3 citations
Journal of Medical Imaging
Retinal Imaging and Analysis
4.08
article

Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification

Nils D. Forkert, Christopher D. Nielsen, Matthias Wilms
article en
3 citations

Abstract

We advance privacy-preserving, ML-based DR screening technology, supporting the goal of equitable vision care worldwide.

Journal of Medical ImagingVol. 12(03)
University of Calgary (CA)
Zero hunger
Openalex Percentile: Top 6%
Retinal Imaging and Analysis
4.08
DOISourceOpenAlex
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Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification — Nils D. Forkert, Christopher D. Nielsen, et al. · Journal of Medical Imaging (2025) | TGRS Research Map | TGRS