Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport

Federated fine‑tuning of large language models (LLMs) over mobile radio access networks (RANs) creates recurring, deadline‑sensitive communication workloads that are currently treated as independent device‑originated flows by transport networks. This mismatch hinders efficient resource provisioning, especially for optical circuit switching and all‑photonics transport that require predictable traffic. The paper proposes that future RANs act as learning‑aware traffic shapers, aggregating asynchronous user‑equipment updates at the gNB to produce fewer, bounded‑size transfers that can be scheduled on dynamically provisioned optical links, thereby combining packet‑based access flexibility with programmable transport capacity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• fine-tuning