{
  "id": 2185,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1857709",
  "title": "FLMMIF: privacy-preserving federated multi-modal medical image fusion",
  "summary": "As a pivotal technique in smart healthcare, medical image fusion integrates complementary functional and structural information to facilitate accurate diagnosis and enhance clinical decision-making reliability. However, existing centralized methods typically raise serious data privacy concerns, while standard distributed approaches often fail to balance global generalization with local node personalization due to data heterogeneity. To address this, we propose FLMMIF, a privacy-preserving framew",
  "authors": "Lei Meng",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-14T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/2185",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1857709",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}