{
  "id": 11339938,
  "title": "Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport",
  "url": "https://urgent.news/2026/10/01/federated-learning-for-llms-over-mobile-networks-issues-and-solutions",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-01T08:37:58.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.01304v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Federated LLM fine-tuning enables large models to be adapted using private and geographically distributed data at the network edge, creating recurring and deadline-sensitive communication workloads across access and transport networks. This challenge is particularly relevant in mobile RANs, where wireless variability, mobility, and device heterogeneity cause model updates to arrive…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}