{
  "id": 12697,
  "url": "https://arxiv.org/abs/2607.19532v1",
  "title": "Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction",
  "summary": "Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predictive models, particularly in personalised cancer care. This research investigates whether federated learning can support the development of robust models for predicting tumour progression in breast cancer patients while addressing four critical deployment pillars: transparency, scalability, security, and fairness. This study evaluates a federated learning fram",
  "authors": "Ruth Amey, Muhammad Arifur Rahman, Taha Osman, Nicholas Shopland, Andy Burton, Mufti Mahmud et al.",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T19:26:36.000Z",
  "fetched_at": "2026-07-23T05:10:49.458Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/12697",
  "original_url": "https://arxiv.org/abs/2607.19532v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}