Health-FedNet: A privacy-preserving federated learning framework for scalable and secure healthcare analytics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258926" target="_blank" >RIV/61989100:27240/25:10258926 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2590123025025538" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123025025538</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.rineng.2025.106484" target="_blank" >10.1016/j.rineng.2025.106484</a>
Alternative languages
Result language
angličtina
Original language name
Health-FedNet: A privacy-preserving federated learning framework for scalable and secure healthcare analytics
Original language description
The growing demand for privacy-preserving healthcare analytics necessitates solutions that comply with global regulatory standards such as HIPAA and GDPR while maintaining high diagnostic accuracy. This paper introduces Health-FedNet, a federated learning (FL) framework designed for secure, decentralized model training across multiple healthcare institutions without transferring raw patient data. Health-FedNet integrates Differential Privacy (DP), Homomorphic Encryption (HE), and an Adaptive Node Weighting Mechanism to enhance privacy, scalability, and robustness against heterogeneous data distributions. Evaluated on the MIMIC-III clinical database, Health-FedNet achieved a 12 % increase in diagnostic accuracy compared to centralized models, with statistical significance confirmed through a Wilcoxon signed-rank test (p < 0.01). The adaptive weighting mechanism prioritizes high-quality data sources, ensuring robust learning and model convergence. Furthermore, Health-FedNet supports real-time streaming updates, optimizing communication overhead and latency, making it suitable for time-sensitive clinical scenarios. Encryption mechanisms are designed to maintain privacy during transmission, aligning with international data protection standards. Future work will explore cross-border scalability, multi-institutional real-time synchronization, and edge-based streaming analytics to further enhance global healthcare collaborations. Health-FedNet represents a scalable, privacy-focused solution for modern healthcare analytics, advancing secure and efficient federated learning in clinical settings.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Results in Engineering
ISSN
2590-1230
e-ISSN
2590-1230
Volume of the periodical
27
Issue of the periodical within the volume
09/2025
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
34
Pages from-to
nestránkováno
UT code for WoS article
001549091700001
EID of the result in the Scopus database
—