@Article{cmes.2026.081705,
AUTHOR = {Karim Gasmi, Afrah Alanazi, Inam Alanazi, Sahar Almenwer, Norah Alanazi, Sarah Almaghrabi, Samia Yahyaoui},
TITLE = {Hybrid Ensemble and Federated Learning Framework for Privacy-Preserving Cardiovascular MRI Segmentation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {147},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/CMES/v147n3/67909},
ISSN = {1526-1506},
ABSTRACT = {Cardiac magnetic resonance imaging (MRI) segmentation is an essential aspect of quantitative cardiovascular analysis, facilitating accurate evaluation of ventricular volumes, myocardial mass, and functional parameters. Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC, but they remain challenging to deploy in real-world multi-centre settings. Data privacy laws make it hard to share data across institutions, and differences in imaging protocols and patient populations mean that data is not always distributed in the same way (non-IID). This can have a big impact on how well models work together and how well they generalise. To address these issues, we first evaluate advanced segmentation architectures, including UNet++ and FPN with EfficientNet-based encoders, and assess multiple hybrid combinations at the probability level. We further improve the ensemble strategy by using a genetic algorithm to automatically identify the optimal model-weighting scheme, rather than fixed combination coefficients. The genetic algorithm explores the solution space to identify the optimal weight configuration based on segmentation metrics. The best hybrid configuration is then chosen as the input architecture for the federated learning stage. We propose a privacy-preserving federated ensemble framework that enables multiple clients to collaboratively train segmentation models without sharing raw MRI data. We methodically evaluate three federated optimisation strategies: FedAvg under IID and non-IID client distributions, and FedProx, which incorporates proximal regularisation to reduce client drift. The genetically optimised ensemble is always used in all federated setups. A thorough analysis of ACDC testing volumes employing overlap- and boundary-based metrics illustrates that the amalgamation of hybrid learning with genetic optimisation and federated training enhances robustness in heterogeneous environments while maintaining data confidentiality, thus providing an efficient approach for secure multi-centre cardiac MRI segmentation.},
DOI = {10.32604/cmes.2026.081705}
}