Federated learning represents an innovative approach to machine learning that prioritizes data privacy. This article introduces this increasingly important technique.

We'll explore how federated learning allows models to be trained across multiple devices or servers without sharing the underlying data, maintaining privacy while still benefiting from collaborative learning.

This approach is particularly valuable in sensitive domains like healthcare and personal devices.