{
  "id": 13591,
  "url": "https://arxiv.org/abs/2509.10517",
  "title": "A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data",
  "summary": "arXiv:2509.10517v3 Announce Type: replace-cross Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use. Federated Learning (FL) is privacy-preserving, yet its behavior under non-IID and imbalanced conditions needs scrutiny. We benchmark five FL strategies - FedAvg, FedProx, FedAdagrad, FedAdam, and FedCluster - for mortality prediction on the MIMIC-IV dataset, partitioning 466,351 admissions across five car",
  "authors": "Rodrigo Tertulino",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T04:00:00.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13591",
  "original_url": "https://arxiv.org/abs/2509.10517",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}