A machine learning approach where models are trained across distributed devices or servers without centralising raw data.
Federated learning is a machine learning approach where models are trained across multiple devices or servers without centralising the underlying data. Instead of moving sensitive data to one location, learning updates are shared and combined to improve the global model.
Federated learning distributes model training across endpoints such as mobile devices, on-premise servers, or regional environments. Each participant keeps local data in place while contributing model improvements. This architecture supports collaborative AI development in environments where privacy, compliance, and data sovereignty are critical.
Secure aggregation and privacy-preserving methods are often added to reduce the risk of exposing sensitive information through model updates.
While federated learning improves privacy posture, it still requires strong governance, secure communication, and robust validation to maintain model quality.
Federated learning is used in AI systems that learn from distributed behaviour patterns while respecting local data controls. Typical examples include mobile keyboard prediction, healthcare analytics, fraud detection, and enterprise intelligence systems where data sharing is restricted.
In language technologies, federated learning can help improve terminology adaptation, predictive typing, and quality estimation across distributed language assets. For translation workflows, it enables teams in different regions to contribute improvements without exposing client texts, supporting secure collaboration for multilingual AI systems.
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