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Federated Learning
A machine learning approach where models are trained across distributed devices or servers without centralising raw data.
Definition
A machine learning approach where models are trained across distributed devices or servers without centralising raw data.
How It Works
Federated Learning helps teams build predictable AI and translation workflows by setting clear expectations for quality, consistency, and decision-making.
In production environments, this concept is applied with process controls such as human review, terminology alignment, and repeatable quality checks across multilingual content.
Key Concepts
- core principle of federated learning
- workflow-level implementation
- terminology and quality consistency
- human validation before publication
Where It Is Used
- localisation workflows
- AI translation pipelines
- multilingual content production
- cross-referencing related concepts such as Fairness and Bias