Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport
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…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
