Health-FedNet: A privacy-preserving federated learning framework for scalable and secure healthcare analytics

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Health-FedNet: A privacy-preserving federated learning framework for scalable and secure healthcare analytics

Popis výsledku v původním jazyce

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 &lt; 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.

Název v anglickém jazyce

Health-FedNet: A privacy-preserving federated learning framework for scalable and secure healthcare analytics

Popis výsledku anglicky

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 &lt; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Svazek periodika

27

Číslo periodika v rámci svazku

09/2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

34

Strana od-do

nestránkováno

Kód UT WoS článku

001549091700001

EID výsledku v databázi Scopus

—