{
  "id": 17708,
  "url": "https://www.sciencedirect.com/science/article/pii/S2666920X26001220?dgcid=rss_sd_all",
  "title": "Federated Learning for Privacy-Preserving At-Risk Student Prediction in Health Professional Education: A Multi-Scenario Evaluation of FedAvg and FedProx",
  "summary": "Publication date: Available online 7 August 2026 Source: Computers and Education: Artificial Intelligence Author(s): Daniel Kwasi Kovor, Eric Opoku Osei, Ebenezer Nana Agyemang",
  "authors": null,
  "category": "research",
  "topics": "privacy-surveillance,healthcare,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-09T05:10:05.170Z",
  "fetched_at": "2026-08-09T05:10:05.170Z",
  "source_slug": "x-computers-and-education-artificial-intel",
  "source_name": "Computers and Education: Artificial Intelligence",
  "source_homepage": "https://www.sciencedirect.com/journal/computers-and-education-artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/17708",
  "original_url": "https://www.sciencedirect.com/science/article/pii/S2666920X26001220?dgcid=rss_sd_all",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}