Federated Learning is a machine learning approach that allows multiple devices to collaboratively train a model while keeping their data decentralized. Instead of sending all data to a central server, each device trains the model locally and only shares the model updates, which helps protect user privacy and reduces data transfer. This method is particularly useful in scenarios where data is sensitive, such as in healthcare or finance. By using Federated Learning, organizations can improve their models without compromising individual data security, enabling better performance while adhering to privacy regulations.