{
  "id": 3259,
  "url": "https://arxiv.org/abs/2606.02958v1",
  "title": "Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries",
  "summary": "Cross-organization language-model adaptation increasingly faces hard governance constraints: in many deployments, device-level model state-parameters, activations, optimizer state, and per-device updates-cannot be exported outside an administrative boundary. Existing distributed and federated stacks typically assume cross-site model exchange and then retrofit privacy mechanisms, which complicates compliance and makes auditing brittle. We present Echelon, a boundary-first training architecture th",
  "authors": "Hina Dixit, Punit Kumar, Irene Tenison, Nevasini Sasikumar",
  "category": "research",
  "topics": "regulation,privacy-surveillance,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-01T23:28:29.000Z",
  "fetched_at": "2026-07-14T16:30:05.532Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3259",
  "original_url": "https://arxiv.org/abs/2606.02958v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}