Machine Learning

Federated Learning

Training models across decentralised devices without centralising their raw data.

Federated learning trains a shared model across phones, hospitals, organisations or other distributed data sources. Each participant updates the model locally and sends only selected model changes to a central coordinator.

The approach can improve privacy because raw data remains on the original device or system. Challenges include communication costs, inconsistent data distributions, unreliable participants and the possibility that model updates may still reveal sensitive information.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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