What is Federated Learning?

Federated Learning is a machine learning approach where a model is trained across multiple decentralized devices or servers that hold local data, without the data ever leaving its original location. Instead of collecting all data in one central location, each device trains the model on its own data and only shares model updates (like adjusted weights) with a central server, which aggregates these updates to improve the global model. The approach improves privacy and security by keeping sensitive data (think health care records, financial information) localized.