Federated Learning is a machine learning approach that allows a model to be trained across multiple decentralised devices or servers holding local data samples, without exchanging them. Instead of sending data to a central server, updates to the model are computed locally on each device, and only model parameters are aggregated or combined. This approach minimises the risk of exposing sensitive user data. It strikes a balance between model performance and data privacy, making it a valuable approach in applications such as healthcare where data privacy is a top priority.