TY  - JOUR
AU  - Grant, Aimie
AU  - Hussain, Amir
AU  - Gogate, Mandar
AU  - Saleem, Nasir
AU  - Dashtipour, Kia
PY  - 2026
DA  - 2026/04/10
TI  - Privacy-Preserving Artificial Intelligence for Diabetes Prediction: A Comparison of Centralised and Federated Learning
JO  - ICCK Transactions on Information Security and Cryptography
T2  - ICCK Transactions on Information Security and Cryptography
JF  - ICCK Transactions on Information Security and Cryptography
VL  - 2
IS  - 2
SP  - 73
EP  - 81
DO  - 10.62762/TISC.2025.335076
UR  - https://www.icck.org/article/abs/TISC.2025.335076
KW  - federated learning
KW  - privacy-preserving AI
KW  - healthcare
KW  - diabetes prediction
AB  - Artificial intelligence (AI) is increasingly used in healthcare to support disease prediction and clinical decision-making. Traditional centralised machine learning approaches often require the aggregation of sensitive patient data into a single repository, which raises substantial privacy, ethical, and regulatory concerns. Federated learning has emerged as a privacy-preserving alternative that enables collaborative model training across distributed data sources without sharing raw patient data. In this study, we investigate whether federated learning can achieve predictive performance comparable to that of centralised machine learning when applied to structured healthcare data. Using the PIMA Indians Diabetes dataset, several centralised machine learning models are evaluated and compared with a federated logistic regression model implemented using the Flower framework. Model performance is assessed using standard classification evaluation metrics. The results show that the federated approach achieves performance comparable to the centralised baselines, with a small reduction in predictive performance. These findings indicate that federated learning is a practical and effective solution for privacy-preserving predictive modelling in healthcare.
SN  - 3070-2429
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Grant2026PrivacyPre,
  author = {Aimie Grant and Amir Hussain and Mandar Gogate and Nasir Saleem and Kia Dashtipour},
  title = {Privacy-Preserving Artificial Intelligence for Diabetes Prediction: A Comparison of Centralised and Federated Learning},
  journal = {ICCK Transactions on Information Security and Cryptography},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {73-81},
  doi = {10.62762/TISC.2025.335076},
  url = {https://www.icck.org/article/abs/TISC.2025.335076},
  abstract = {Artificial intelligence (AI) is increasingly used in healthcare to support disease prediction and clinical decision-making. Traditional centralised machine learning approaches often require the aggregation of sensitive patient data into a single repository, which raises substantial privacy, ethical, and regulatory concerns. Federated learning has emerged as a privacy-preserving alternative that enables collaborative model training across distributed data sources without sharing raw patient data. In this study, we investigate whether federated learning can achieve predictive performance comparable to that of centralised machine learning when applied to structured healthcare data. Using the PIMA Indians Diabetes dataset, several centralised machine learning models are evaluated and compared with a federated logistic regression model implemented using the Flower framework. Model performance is assessed using standard classification evaluation metrics. The results show that the federated approach achieves performance comparable to the centralised baselines, with a small reduction in predictive performance. These findings indicate that federated learning is a practical and effective solution for privacy-preserving predictive modelling in healthcare.},
  keywords = {federated learning, privacy-preserving AI, healthcare, diabetes prediction},
  issn = {3070-2429},
  publisher = {Institute of Central Computation and Knowledge}
}