Federated learning is a distributed learning approach where multiple participants (contributors) collaborate to collectively train a machine learning model without sharing their local data directly.

Instead of sending raw data to a central server, federated learning involves sending model parameters or updates between the central server and the contributors. This unique approach offers several advantages, including privacy preservation, reduced communication overhead, and the ability to harness the power of decentralised resources.