{
  "id": 6871,
  "url": "https://arxiv.org/abs/2603.21656v1",
  "title": "TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints",
  "summary": "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",
  "authors": "Vagish Kumar, Syed Bahauddin Alam, Souvik Chakraborty",
  "category": "research",
  "topics": "regulation,privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-23T07:34:45.000Z",
  "fetched_at": "2026-07-14T16:32:50.143Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6871",
  "original_url": "https://arxiv.org/abs/2603.21656v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}