{
  "id": 18024,
  "url": "https://arxiv.org/abs/2608.09221v1",
  "title": "FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning",
  "summary": "Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces significant challenges due to data heterogeneity, particularly in terms of label distribution skewness and variations in dataset sizes, which can lead to biased model updates and hinder convergence. To address this, we propose FedTVD, a novel FL algorithm that weights client contributions during aggregation by considering both data quality and quantity.",
  "authors": "Radwan Selo, Majid Kundroo, Taehong Kim",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T07:45:25.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18024",
  "original_url": "https://arxiv.org/abs/2608.09221v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}