{
  "id": 15880,
  "url": "https://arxiv.org/abs/2608.02250v1",
  "title": "Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning",
  "summary": "Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models. FL enables decentralized training while preserving the privacy of clients' datasets. However, non-independent and identically distributed (non-IID) or noisy datasets can lead to low model accuracy or high convergence latency. Precluding these clients through client selection may mitigate the problem, but heavily biased client select",
  "authors": "Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei Chen",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T13:58:35.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/15880",
  "original_url": "https://arxiv.org/abs/2608.02250v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}