MediNet: simplifying federated and privacy-preserving AI deployment in healthcare
5 affiliations
- 1Barcelona Institute for Global Health (ISGlobal) Bioinformatic Research Group in Epidemiology (BRGE), , Barcelona, 08003,
- 2BigOmics Analytics SA
- 3University of Oviedo Department of Mathematics, Faculty of Sciences, , Oviedo, 33007,
- 4Institute of Space Sciences and Technologies of Asturias. (ICTEA), University of Oviedo , Oviedo, 33004,
- 5Instituto de Salud Carlos III CIBER en Epidemiología y Salud Pública (CIBERESP), , Madrid, 28029,
Abstract
Motivation: The application of Artificial Intelligence and Deep Learning (DL) in healthcare is increasingly feasible in principle, yet remains inaccessible in practice for most clinical institutions. Beyond the well-known challenge of data privacy regulations that prevent centralization of patient records, two further barriers limit adoption. First, deploying a federated learning infrastructure requires substantial technical expertise: configuring distributed training environments, implementing privacy-preserving mechanisms such as Differential Privacy (DP), and managing secure inter-institutional communication demands specialized knowledge that most healthcare organizations do not possess. Second, designing and configuring DL models (selecting architectures, tuning hyperparameters, interpreting results) currently requires machine learning expertise that clinical professionals, including researchers and physicians, typically lack. Existing federated learning frameworks address the infrastructure problem but impose a steep learning curve that effectively excludes non-specialized users. A solution that abstracts this complexity, enabling clinicians and biomedical researchers to design, launch, and monitor federated training processes without programming or machine learning expertise, remains absent from the field.
Results: To address existing limitations, we introduce MediNet, a comprehensive server–client Federated learning (FL) platform that allows hospitals and research centers to train Machine Learning (ML) and DL models without moving or exposing sensitive data. It offers an intuitive web-based environment that simplifies FL, enabling users to easily select the model and define training criteria while administrators manage permissions and datasets, ensuring granular and autonomous data control. The system automatically constructs and generates the underlying technical configuration (e.g. Python/FL scripts) according to the parameters specified in the GUI. It then orchestrates the secure federated rounds and provides real-time monitoring, fully encapsulating the complexity of deployment and security. MediNet is built upon PyTorch as its primary framework, chosen for its robustness in DL model development along with its Differential Privacy (DP) libraries. Flower AI (Beutel et al. 2020) is used as the federated orchestration system. These underlying technologies are internal pillars of MediNet, essential for ensuring robustness, functionality, and strict adherence to privacy principles.
Availability and implementations: The general information page for the MediNet software is available at https://isglobal-brge.github.io/MediNet. The MediNet software and its complementary tools are fully available under the MIT license on GitHub. The MediNetHub and MediNetNode code can be found on https://github.com/isglobal-brge/MediNetHub and https://github.com/isglobal-brge/MediNetNode.
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BibTeX
@article{mateonavarro2026medinet,
title = {{MediNet: simplifying federated and privacy-preserving AI deployment in healthcare}},
author = {Ramon Mateo-Navarro and David Sarrat-González and Dolors Pelegrí-Sisó and Xavier Escribà-Montagut and Fernando Sánchez-Lasheras and Juan R González},
journal = {Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag742},
url = {https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag742/8868964},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - MediNet: simplifying federated and privacy-preserving AI deployment in healthcare
AU - Ramon Mateo-Navarro
AU - David Sarrat-González
AU - Dolors Pelegrí-Sisó
AU - Xavier Escribà-Montagut
AU - Fernando Sánchez-Lasheras
AU - Juan R González
PY - 2026
DA - 2026/10/06
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag742
UR - https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag742/8868964
AB - Motivation: The application of Artificial Intelligence and Deep Learning (DL) in healthcare is increasingly feasible in principle, yet remains inaccessible in practice for most clinical institutions. Beyond the well-known challenge of data privacy regulations that prevent centralization of patient records, two further barriers limit adoption. First, deploying a federated learning infrastructure requires substantial technical expertise: configuring distributed training environments, implementing privacy-preserving mechanisms such as Differential Privacy (DP), and managing secure inter-institutional communication demands specialized knowledge that most healthcare organizations do not possess. Second, designing and configuring DL models (selecting architectures, tuning hyperparameters, interpreting results) currently requires machine learning expertise that clinical professionals, including researchers and physicians, typically lack. Existing federated learning frameworks address the infrastructure problem but impose a steep learning curve that effectively excludes non-specialized users. A solution that abstracts this complexity, enabling clinicians and biomedical researchers to design, launch, and monitor federated training processes without programming or machine learning expertise, remains absent from the field. Results: To address existing limitations, we introduce MediNet, a comprehensive server–client Federated learning (FL) platform that allows hospitals and research centers to train Machine Learning (ML) and DL models without moving or exposing sensitive data. It offers an intuitive web-based environment that simplifies FL, enabling users to easily select the model and define training criteria while administrators manage permissions and datasets, ensuring granular and autonomous data control. The system automatically constructs and generates the underlying technical configuration (e.g. Python/FL scripts) according to the parameters specified in the GUI. It then orchestrates the secure federated rounds and provides real-time monitoring, fully encapsulating the complexity of deployment and security. MediNet is built upon PyTorch as its primary framework, chosen for its robustness in DL model development along with its Differential Privacy (DP) libraries. Flower AI (Beutel et al. 2020) is used as the federated orchestration system. These underlying technologies are internal pillars of MediNet, essential for ensuring robustness, functionality, and strict adherence to privacy principles. Availability and implementations: The general information page for the MediNet software is available at https://isglobal-brge.github.io/MediNet. The MediNet software and its complementary tools are fully available under the MIT license on GitHub. The MediNetHub and MediNetNode code can be found on https://github.com/isglobal-brge/MediNetHub and https://github.com/isglobal-brge/MediNetNode.
SN - 1367-4803
SN - 1367-4811
ER -