TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints

Protecting patient privacy remains a fundamental barrier to scaling machine learning across healthcare institutions, where centralizing sensitive data is often infeasible due to ethical, legal, and regulatory constraints. Federated learning offers a promising alternative by enabling privacy-preserving, multi-institutional training without sharing raw patient data; however, real-world deployments face severe challenges from data heterogeneity, site-specific biases, and class imbalance, which degr

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ethics.ai (23 March 2026), “TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints,” evidence record 6871, https://ethics.ai/record/6871 (originally published by arXiv).

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