Federated Learning for Distributed Network AI

Machine learning frameworks where edge nodes, base stations, and devices collaboratively train models by exchanging gradient updates rather than raw data. Federated learning preserves privacy, reduces backhaul traffic, and enables personalized network optimization at scale across operator boundaries.