Menu

Tag: Federated Learning

Federated Learning is a machine learning approach that enables multiple decentralized devices or servers to collaboratively learn a shared model while keeping their data locally. Instead of gathering data into a single central server for training, federated learning allows each participant to train a model on its own data and then share only the model updates or parameters with a central server. This process preserves data privacy and security, as raw data is never transmitted over the network. It is particularly useful in scenarios where data is sensitive, such as in healthcare or finance, or when data is distributed across various locations, like mobile devices. Federated Learning aims to provide a way to leverage data from multiple sources to improve model performance without compromising individual privacy.

Lamovida Radio - Proudly Powered by WordPress
Theme by Grace Themes