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 asynchronously. Although these updates belong to the same learning round and share a common destination and deadline, conventional transport networks treat them as independent device-originated flows, hiding their underlying structure and limiting the ability to efficiently provision transport resources. This m...
| Authors | Emilio Paolini, Andrea Pinto, Flavio Esposito et al. |
|---|---|
| Primary category | cs.NI |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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