Federated Learning is a machine learning technique where the training process of a model is distributed across multiple devices or servers, each holding their own local dataset. Rather than sending data to a central server, the devices compute the model updates locally and share only these updates (gradients) with the server. This methodology enhances data privacy as sensitive information does not leave the local environment, making it particularly useful in industries such as healthcare, finance, and technology where data privacy regulations are stringent.