Federated Learning

A machine learning technique in which a model is trained across multiple devices or servers without the raw training data ever leaving those devices. Instead of centralizing data, federated learning sends the model to the data, collects local model updates (gradients), and aggregates them centrally — preserving the privacy of individual data sources. It is widely used in healthcare (where patient data cannot be shared across hospitals), finance (where transaction data is sensitive), and mobile devices (where user data stays on-device). From a governance perspective, federated learning reduces data privacy risk but creates new model governance challenges: the training process is distributed and harder to audit.

A healthcare consortium trains a disease detection model using federated learning: each hospital trains on its own patient data locally, and only the model improvements — not patient records — are shared with the central coordinator.