Federated AI for Healthcare: Secure and Scalable Data Integration in Cloud Environments
Abstract
Federated Artificial Intelligence (AI) has emerged as a transformative paradigm for enabling secure and scalable data integration in healthcare cloud environments. Traditional centralized AI models require aggregating sensitive patient data into a single repository, raising concerns related to privacy, regulatory compliance, and data breaches. Federated learning (FL) addresses these challenges by allowing multiple healthcare institutions to collaboratively train machine learning models without sharing raw data. Instead, local models are trained on-site and only model parameters are exchanged and aggregated, ensuring data confidentiality while leveraging distributed datasets.
In cloud-based healthcare systems, federated AI enhances interoperability across heterogeneous data sources such as electronic health records (EHRs), medical imaging systems, and wearable devices. The integration of advanced privacy-preserving techniques, including differential privacy and homomorphic encryption, further strengthens security in distributed environments.
This study explores the architecture, implementation strategies, and challenges of federated AI in healthcare cloud ecosystems. It also examines scalability considerations, communication efficiency, and governance frameworks required for real-world deployment. By enabling collaborative intelligence without compromising patient privacy, federated AI offers a sustainable solution for next-generation healthcare analytics, improving diagnosis accuracy, treatment personalization, and overall patient outcomes in a secure and compliant manner.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
17790-17799 |
Published |
December 12, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Zeinab E.Ahmed (2025). Federated AI for Healthcare: Secure and Scalable Data Integration in Cloud Environments. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17790-17799. https://doi.org/10.15662/IJAESIT.2025.0806011 |
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