Federated Learning is a machine learning approach where multiple devices or organizations train a shared model without sending their raw data to a central location. Each participant trains the model locally and sends only model updates, helping keep sensitive data on the original device or system.
For example, smartphones can collaboratively improve a predictive keyboard by training on local typing patterns and sharing model updates rather than personal messages. Federated Learning is useful in healthcare, finance, mobile applications, and IoT, where privacy, data security, and distributed data processing are important.
