Federated Learning, Part 1: The Basics of Training Models Where the Data Lives

Federated learning (FL) is a machine learning technique that trains models on decentralized data without moving it to a central server, addressing privacy and access issues. This approach is particularly useful in sensitive domains like healthcare, where models can be trained locally within hospitals, and only model updates are shared. The process involves a central server coordinating training rounds: it sends a global model to clients, they train it on local data, and then send back only the updated parameters. The server aggregates these updates, often using an algorithm like Federated Averaging, to create an improved global model for the next round.