{
  "id": 12641,
  "url": "https://arxiv.org/abs/2607.19403",
  "title": "Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets",
  "summary": "arXiv:2607.19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings. A prior architectural study by the same authors (Tertulino and Alencar, 2026) demonstrated, on a synthetic six-feature benchmark, that server-side adaptive optimization acts as a temporal denoiser for Differential Privacy noise, answ",
  "authors": "Rodrigo Tertulino, Laercio Alencar, Ricardo Almeida",
  "category": "research",
  "topics": "privacy-surveillance,healthcare,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T04:00:00.000Z",
  "fetched_at": "2026-07-23T05:10:49.458Z",
  "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/12641",
  "original_url": "https://arxiv.org/abs/2607.19403",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}