{
  "id": 4322,
  "url": "https://arxiv.org/abs/2605.14886v1",
  "title": "BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring",
  "summary": "Electrocardiogram (ECG) monitoring in Internet of Medical Things (IoMT) networks is constrained by strict data-sharing regulations and privacy concerns. Federated learning (FL) enables collaborative learning by keeping raw ECG data on devices, but frequent transmissions of high-dimensional model updates incur heavy per-round traffic over bandwidth-limited links. To alleviate this bottleneck, federated distillation (FD) replaces parameter exchange with logit-based knowledge transfer. However, the",
  "authors": "Zixuan Shu, Tiancheng Cao, Hen-Wei Huang",
  "category": "research",
  "topics": "regulation,privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-14T14:31:02.000Z",
  "fetched_at": "2026-07-14T16:30:54.921Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4322",
  "original_url": "https://arxiv.org/abs/2605.14886v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}