Federated Learning

Federated Learning is a privacy-preserving machine learning (ML) approach in which numerous devices or entities work together to train a shared model without sharing their raw data. To ensure data privacy while facilitating collaborative learning, the model is instead trained locally on each device, with only the updated model parameters communicated to a central server.