Federated Neural Learning Architectures for Scalable and Privacy-Preserving Analysis of Distributed Health Data in Healthcare Systems

Vincent Koc

doi:10.62762/TNC.2025.916035

Article Information

Published in ICCK Transactions on Neural Computing

Volume/Issue Volume 1, Issue 2, 2025

Pages 108-117

Abstract

In recent years, the use of the Internet of Medical Things (IoMT) and electronic health records (EHRs) has created exhaustive sensitive healthcare data. If this data is analyzed in an effective way, it will improve the prediction of diseases, the recovery of patients, and the personalization of medicine. However, the collection of data in a central manner brings with it some serious problems related to privacy, security, and rules. Federated Learning (FL), the machine learning approach that is decentralized, seems to be a solution in which model training is carried out in a collaborative way without sharing any raw data. The application of FL in distributed health data analysis is the subject of this paper, which, however, will mainly focus on its ability to combine data privacy and analytical precision. The key contributions of this paper include offering some insights into FL's implementation in the healthcare industry, discussing its benefits and drawbacks, and providing a review of some state-of-the-art methods concerning data security, communication efficiency, and scalability. Furthermore, examples of disease prediction and health monitoring will be pointed out to show the application of FL in the real world. The results suggest that FL has the potential to fundamentally change the field of health data analysis through the use of a collaborative machine learning framework that is secure, privacy-preserving, and allows sharing among parties. Lastly, some future directions and potential improvements for FL in healthcare have also been set out.

Graphical Abstract

Federated Neural Learning Architectures for Scalable and Privacy-Preserving Analysis of Distributed Health Data in Healthcare Systems

Keywords

federated learning healthcare data privacy preservation machine learning distributed analysis

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

Vincent Koc is an employee of Hyperthink Labs, San Francisco, CA, United States.

Ethical Approval and Consent to Participate

Not applicable.

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Cite This Article

APA Style

Koc, V. (2025). Federated Neural Learning Architectures for Scalable and Privacy-Preserving Analysis of Distributed Health Data in Healthcare Systems. ICCK Transactions on Neural Computing, 1(2), 108–117. https://doi.org/10.62762/TNC.2025.916035

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TY  - JOUR
AU  - Koc, Vincent
PY  - 2025
DA  - 2025/06/30
TI  - Federated Neural Learning Architectures for Scalable and Privacy-Preserving Analysis of Distributed Health Data in Healthcare Systems
JO  - ICCK Transactions on Neural Computing
T2  - ICCK Transactions on Neural Computing
JF  - ICCK Transactions on Neural Computing
VL  - 1
IS  - 2
SP  - 108
EP  - 117
DO  - 10.62762/TNC.2025.916035
UR  - https://www.icck.org/article/abs/TNC.2025.916035
KW  - federated learning
KW  - healthcare data
KW  - privacy preservation
KW  - machine learning
KW  - distributed analysis
AB  - In recent years, the use of the Internet of Medical Things (IoMT) and electronic health records (EHRs) has created exhaustive sensitive healthcare data. If this data is analyzed in an effective way, it will improve the prediction of diseases, the recovery of patients, and the personalization of medicine. However, the collection of data in a central manner brings with it some serious problems related to privacy, security, and rules. Federated Learning (FL), the machine learning approach that is decentralized, seems to be a solution in which model training is carried out in a collaborative way without sharing any raw data. The application of FL in distributed health data analysis is the subject of this paper, which, however, will mainly focus on its ability to combine data privacy and analytical precision. The key contributions of this paper include offering some insights into FL's implementation in the healthcare industry, discussing its benefits and drawbacks, and providing a review of some state-of-the-art methods concerning data security, communication efficiency, and scalability. Furthermore, examples of disease prediction and health monitoring will be pointed out to show the application of FL in the real world. The results suggest that FL has the potential to fundamentally change the field of health data analysis through the use of a collaborative machine learning framework that is secure, privacy-preserving, and allows sharing among parties. Lastly, some future directions and potential improvements for FL in healthcare have also been set out.
SN  - 3068-7519
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Koc2025Federated,
  author = {Vincent Koc},
  title = {Federated Neural Learning Architectures for Scalable and Privacy-Preserving Analysis of Distributed Health Data in Healthcare Systems},
  journal = {ICCK Transactions on Neural Computing},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {108-117},
  doi = {10.62762/TNC.2025.916035},
  url = {https://www.icck.org/article/abs/TNC.2025.916035},
  abstract = {In recent years, the use of the Internet of Medical Things (IoMT) and electronic health records (EHRs) has created exhaustive sensitive healthcare data. If this data is analyzed in an effective way, it will improve the prediction of diseases, the recovery of patients, and the personalization of medicine. However, the collection of data in a central manner brings with it some serious problems related to privacy, security, and rules. Federated Learning (FL), the machine learning approach that is decentralized, seems to be a solution in which model training is carried out in a collaborative way without sharing any raw data. The application of FL in distributed health data analysis is the subject of this paper, which, however, will mainly focus on its ability to combine data privacy and analytical precision. The key contributions of this paper include offering some insights into FL's implementation in the healthcare industry, discussing its benefits and drawbacks, and providing a review of some state-of-the-art methods concerning data security, communication efficiency, and scalability. Furthermore, examples of disease prediction and health monitoring will be pointed out to show the application of FL in the real world. The results suggest that FL has the potential to fundamentally change the field of health data analysis through the use of a collaborative machine learning framework that is secure, privacy-preserving, and allows sharing among parties. Lastly, some future directions and potential improvements for FL in healthcare have also been set out.},
  keywords = {federated learning, healthcare data, privacy preservation, machine learning, distributed analysis},
  issn = {3068-7519},
  publisher = {Institute of Central Computation and Knowledge}
}

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