{
  "id": 6593,
  "url": "https://arxiv.org/abs/2603.28455v1",
  "title": "FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation",
  "summary": "In federated healthcare systems, Federated Class-Incremental Learning (FCIL) has emerged as a key paradigm, enabling continuous adaptive model learning among distributed clients while safeguarding data privacy. However, in practical applications, data across agent nodes within the distributed framework often exhibits non-independent and identically distributed (non-IID) characteristics, rendering traditional continual learning methods inapplicable. To address these challenges, this paper covers ",
  "authors": "Tiantian Wang, Xiang Xiang, Simon S. Du",
  "category": "research",
  "topics": "privacy-surveillance,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-30T13:58:36.000Z",
  "fetched_at": "2026-07-14T16:32:37.308Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6593",
  "original_url": "https://arxiv.org/abs/2603.28455v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}