Distributed Learning
A machine learning approach where training data is distributed across multiple devices or locations, and models are trained collaboratively without sharing raw data.
Includes federated learning and split-learning: each node trains locally on private data and shares model updates or embeddings with a central server. This preserves data privacy and reduces bandwidth, but requires secure aggregation, drift handling, and governance for update validation to prevent poisoned-update attacks.
A healthcare consortium trains a disease-prediction model across five hospitals using federated learning. Each hospital trains on its patient records locally, sends encrypted weight updates, and the central server aggregates them - enabling a robust model without exposing any patient data externally.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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