Federated Learning is a method of training artificial intelligence models across multiple devices or organizations without transferring raw data to a central server. Instead, each participant trains the model locally using their own data, and only shares model updates, such as learned patterns or parameters. These updates are then aggregated to improve the global model. This approach enhances privacy and security, as sensitive information remains on local devices, reducing the risk of data breaches and ensuring compliance with data protection regulations.