Description
Federated learning has become one of the most significant developments in modern artificial intelligence, offering a practical solution for collaborative model training while preserving the privacy of distributed data. As digital systems increasingly rely on vast amounts of user-generated information, the limitations of centralized machine learning are becoming more evident, particularly with respect to data security, regulatory compliance, and communication constraints. Federated learning addresses these challenges by shifting computation to the data source, enabling devices and organizations to contribute to model development without sharing raw data.
