{
  "id": 12695,
  "url": "https://arxiv.org/abs/2607.19621v1",
  "title": "Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub",
  "summary": "Federated Learning (FL) enables collaborative model training without centralizing raw data, but building and operating FL systems remains difficult due to distributed execution, rapidly evolving frameworks, and privacy and governance requirements. In this paper, we present an empirical study of FL developer challenges by independently analyzing 495 Stack Overflow posts and 9,116 GitHub issues and pull requests from 92 FL-related projects. Using BERTopic-based topic modeling and difficulty indica",
  "authors": "Sahand Saed, Khairul Alam, Banani Roy",
  "category": "research",
  "topics": "regulation,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T23:06:00.000Z",
  "fetched_at": "2026-07-23T05:10:49.458Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/12695",
  "original_url": "https://arxiv.org/abs/2607.19621v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}