Federated learning is a technique where multiple organizations collaboratively train an AI model without sharing their raw data. Each participant trains a local copy of the model on their own data, then shares only the model updates — not the data itself — with a central server that aggregates them. It is like several hospitals each studying their own patient records and then pooling what they learned, without any patient file ever leaving the hospital. Privacy and regulatory compliance are the main drivers.