Balancing privacy and performance in healthcare: A federated learning framework for sensitive data
This PPFL framework enables effective, trustworthy privacy-preserving ML in healthcare and resource-constrained settings. Future work will extend model architectures, regulatory alignment, and direct user trust assessment.
Authors
- Faisal Iradat
- Haya Abdullah A. Alhakbani
- Waseem Iqbal (ORCID: https://orcid.org/0000-0002-3616-2621)
- Fakhri Alam Khan (ORCID: https://orcid.org/0000-0002-9130-1874)
- Hatoon S. AlSagri (ORCID: https://orcid.org/0000-0003-3753-5116)
- Awais Ahmad (ORCID: https://orcid.org/0000-0001-5483-2732)
- Fatima Tanveer
Institutions
- King Fahd University of Petroleum and Minerals (SA)
- Institute of Business Administration Karachi (PK)
- Imam Mohammad ibn Saud Islamic University (SA)
- Sultan Qaboos University (OM)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251381769
- Citations
- 2
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 4.96