Federated learning: Difference between revisions
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Latest revision as of 00:24, 20 January 2026
Federated learning
A type of machine learning in which a model is trained in a decentralized fashion using multiple data sources without pooling or combining the data in any centralized location. Federated learning allows entities or devices to collaboratively train a global model by exchanging model updates without directly sharing the data that each entity controls.
Source: NIST AI 100-2e2025 | Category: