{
  "id": 17821,
  "url": "https://arxiv.org/abs/2608.06876v1",
  "title": "FedVAR: Prototype-Aligned Federated Framework for Video Anomaly Recognition",
  "summary": "In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intelligence paradigm for Video Anomaly Recognition (VAR). This task is vital for maintaining high-fidelity Digital Twins and ensuring safety in mission-critical environments. However, the inherent data heterogeneity across distributed edge clients leads to a fundamental challenge known as semantic misalignment, where clients learn divergent feature repres",
  "authors": "Ghani Haider, Majid Kundroo, Boyun Eom, Dong Hwan Park, Chen Chen, Taehong Kim",
  "category": "research",
  "topics": "safety-alignment,military-security,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T07:01:42.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17821",
  "original_url": "https://arxiv.org/abs/2608.06876v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}