Manually tuning thousands of base stations is operationally impossible. A federated learning workflow automates the collaborative training of local AI models—for handover prediction or beam management—without sharing raw user data. The central orchestrator initiates cycles, dispatches global model updates, and aggregates learned parameters from each distributed RAN node. This privacy-preserving approach directly reduces the compliance overhead and data-transfer costs that stall traditional RAN optimization projects, turning distributed intelligence into a scalable operating model.




