What is a common application of federated learning?

Federated learning is a distributed machine learning approach that allows models to be trained across multiple devices or servers while keeping the data localized. This method enables the training of AI models using data from individual devices, like smartphones, without needing to transfer sensitive personal data to a central server. By utilizing local user data, federated learning can improve features such as personalized recommendations, predictive text, or voice recognition systems, enhancing user experience while maintaining privacy.

The other options do not accurately reflect the principles of federated learning. For example, training AI models on centralized databases involves aggregating data in one location, which contradicts the basic premise of federated learning that emphasizes data locality. Analyzing public datasets for trends typically involves comprehensive data gathering from a single source or multiple sources, rather than a federated approach which deals with sensitive or distributed data. Lastly, sharing personal data across different devices directly opposes the key advantage of federated learning, which is to keep user data on individual devices, thus enhancing privacy and security.