Federated Learning

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Federated learning is a way to train a shared machine learning model without collecting everyone’s data in one place. Each participant keeps data locally and trains the model on that data. Instead of sending raw records to a central server, participants send training updates, which are combined to improve the shared global model. Because the original data stays where it is, this approach fits situations where data is sensitive or difficult to transfer.

This setup is often used across many devices, such as smartphones, or across organizations that want to learn from a wider pool of data without sharing their internal datasets. In real deployments, federated learning is commonly paired with privacy and security techniques that strengthen protection while supporting large-scale collaboration.

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