August 11, 2025

Federated Learning for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges

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Federated learning significantly enhances medical image analysis while addressing data scarcity and privacy issues.
This survey categorizes FL methodologies into three pillars—training, architecture, and unlearning—for clarity and usability.
Comprehensive evaluation of federated learning methods indicates their potential to meet stringent privacy regulations like HIPAA and GDPR.
The findings underscore a balance between technical rigor and the practical needs of the medical domain, paving the way for effective application.

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Survey organizes federated learning methods for medical image analysis, highlighting privacy and clinical challenges.

Hu et al. (2025) studied this question.

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