{
  "id": 13731,
  "url": "https://arxiv.org/abs/2607.21647v1",
  "title": "A Drift Stable Quantum Federated Learning for Intelligent Services",
  "summary": "Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services. Intelligent services in this context refer to privacy-sensitive distributed decision systems, such as fraud detection and genomic classification, where reliable and fair client-level learning is as important as the accuracy of the aggregate model. However, heterogeneous client data and noisy quantum optimization often caus",
  "authors": "Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel",
  "category": "research",
  "topics": "privacy-surveillance,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-22T01:44:00.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/13731",
  "original_url": "https://arxiv.org/abs/2607.21647v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}