Federated learning and differential privacy: Machine learning and deep learning for biomedical image data classification

Federated learning with differential privacy offers a promising solution for secure and accurate biomedical image classification, supporting privacy-preserving machine learning in medical diagnostics without compromising performance.

Authors

Ying Han (ORCID: https://orcid.org/0000-0003-3046-9507)
Hu Dongyan
Fei Pan (ORCID: https://orcid.org/0000-0003-2289-9865)
Sobia Wassan (ORCID: https://orcid.org/0000-0001-5504-7496)
Liudajun

Publication Details

Journal: Digital Health
Published: 2025-05-01
DOI: https://doi.org/10.1177/20552076251358531
Citations: 3
Primary Topic: Privacy-Preserving Technologies in Data
Type: article
Field-Weighted Citation Impact: 7.44

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article

Federated learning and differential privacy: Machine learning and deep learning for biomedical image data classification

Ying Han, Hu Dongyan, Fei Pan, Sobia Wassan et al.

May 1, 2025

3 citations

Digital Health

Privacy-Preserving Technologies in Data

7.44

article

Federated learning and differential privacy: Machine learning and deep learning for biomedical image data classification

Ying Han, Hu Dongyan, Fei Pan, Sobia Wassan, Liudajun

May 1, 2025article en

3 citations

Abstract

Digital HealthVol. 11

Openalex Percentile: Top 3%

Privacy-Preserving Technologies in Data

7.44

DOI Open Access PDF OpenAlex

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Federated learning and differential privacy: Machine learning and deep learning for biomedical image data classification — Ying Han, Hu Dongyan, et al. · Digital Health (2025) | TGRS Research Map | TGRS