Citations refer to the original publication, not to a Scieee localized version.
Sinaci, A. A., Gencturk, M., Alvarez-Romero, C., Laleci Erturkmen, G. B., Martinez-Garcia, A., Escalona Cuaresma, M. J., & Parra-Calderon, C. L. (2024). Privacy-preserving federated machine learning on FAIR health data: A real-world application. Elsevier. https://doi.org/10.1016/j.csbj.2024.02.014
Sinaci, A. Anil, et al. Privacy-preserving federated machine learning on FAIR health data: A real-world application. Elsevier, 2024. https://doi.org/10.1016/j.csbj.2024.02.014.
Sinaci, A. Anil, Mert Gencturk, Celia Alvarez-Romero, Gokce Banu Laleci Erturkmen, Alicia Martinez-Garcia, María José Escalona Cuaresma, and Carlos Luis Parra-Calderon. Privacy-preserving federated machine learning on FAIR health data: A real-world application. Elsevier, 2024. https://doi.org/10.1016/j.csbj.2024.02.014.
Sinaci, A. A. et al. (2024) Privacy-preserving federated machine learning on FAIR health data: A real-world application. Elsevier. Available at: https://doi.org/10.1016/j.csbj.2024.02.014.
A. A. Sinaci et al., “Privacy-preserving federated machine learning on FAIR health data: A real-world application,” Elsevier, 2024, doi: 10.1016/j.csbj.2024.02.014.
@misc{sinaci2024privacypreserving,
author = {Sinaci, A. Anil and Gencturk, Mert and Alvarez-Romero, Celia and Laleci Erturkmen, Gokce Banu and Martinez-Garcia, Alicia and Escalona Cuaresma, María José and Parra-Calderon, Carlos Luis},
title = {Privacy-preserving federated machine learning on FAIR health data: A real-world application},
year = {2024},
publisher = {Elsevier},
doi = {10.1016/j.csbj.2024.02.014},
url = {https://doi.org/10.1016/j.csbj.2024.02.014}
}
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