How does federated learning enable collaborative model training across distributed devices or edge devices?

Federated learning allows machine learning models to be trained collaboratively across multiple devices or edge nodes without centralized data aggregation. Instead of sending raw data to a central server for training, federated learning aggregates local model updates or gradients computed on individual devices, preserving data privacy, reducing communication overhead, and enabling personalized model training on edge devices with limited connectivity or bandwidth.