TY - JOUR
T1 - Federated Learning for Healthcare: A Critical Review of Privacy Guarantees, Heterogeneity Challenges, and the Research–Deployment Gap
AU - Khaled Mahfouz
AU - Rania Abdelaziz
AU - Sherif Adel
AU - Dina Mostafa
AU - Tamer Nabil
AU - Reem Saad
JF - Journal of Artificial Intelligence for Healthcare Systems
JO - J. Artif. Intell. Healthc. Syst.
SN - 3149-8981
Y1 - 2025
VL - 4
IS - 1
SP - 97
N2 - Federated learning (FL) is promoted as a privacy-preserving method for training machine learning models across healthcare institutions without sharing patient data, with growing use in medical imaging, electronic health records, and rare disease research. This critical review examines FL studies from 2017–2024, focusing on privacy guarantees, statistical heterogeneity, communication efficiency, and real-world clinical deployment. A structured search of PubMed, IEEE Xplore, arXiv, and Google Scholar was conducted using relevant FL and healthcare terms, including studies addressing privacy, heterogeneity, communication, or deployment. Reported privacy guarantees are often overstated, with most studies relying on FedAvg without differential privacy. Statistical heterogeneity in non-IID settings remains largely unresolved. Fewer than 5% of studies report real-world deployment, typically at very small scale. A significant gap exists between FL research and clinical application. Current methods fall short of healthcare-grade privacy and real-world constraints, limiting readiness for high-stakes clinical use.
UR - https://cirpublications.com/i028497975
ER -