{
  "id": 18076,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1895239",
  "title": "GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT",
  "summary": "Visual object detection is essential for defect inspection and process monitoring in edge-deployed Industrial Internet of Things (IIoT). Yet, training accurate detectors across distributed factories faces stringent constraints on data privacy, annotation budgets, and uplink communication. Standard federated learning (FL) preserves locality but often wastes labeling resources on redundant frames and overlooks detection-specific gradient alignment when scheduling clients. To bridge this gap, we pr",
  "authors": "Zepeng Wang",
  "category": "research",
  "topics": "safety-alignment,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T00:00:00.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/18076",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1895239",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}