Federated learning is an approach to machine learning that addresses data governance and privacy concerns by enabling the collaborative training of algorithms without transferring data to a central location. In this model, each device trains on data locally and shares its local model parameters instead of sharing the training data, and different federated learning systems have various topologies for parameter sharing.