{
  "id": 18300,
  "url": "https://arxiv.org/abs/2608.07591v1",
  "title": "Zero-Trust Federated Learning for Connected Aftermarket Devices",
  "summary": "Connected aftermarket devices extend vehicle diagnostics, repair workflows, and over-the-air software maintenance beyond original equipment manufacturer boundaries, yet their heterogeneous ownership and long service life complicate conventional perimeter security. This paper develops Zero Trust Federated Learning for Connected Aftermarket Devices (ZT FL CADE), an edge-learning architecture that combines device-level access control, privacy-preserving federated learning, and adversarial validatio",
  "authors": "Shunmukha Sagar Puppala",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T20:10:12.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-arxiv-cs-cr-ai-security",
  "source_name": "arXiv cs.CR (AI security)",
  "source_homepage": "https://arxiv.org/list/cs.CR/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18300",
  "original_url": "https://arxiv.org/abs/2608.07591v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}