Federated Learning with Flower Framework

Federated learning is a way to train machine learning models across multiple devices without sharing their data. Instead of sending data to a central server, each device trains the model locally and only shares the model updates, keeping the data private.

The Flower Federated Framework is a system designed to help different devices and organizations work together to improve machine learning models while keeping data private. Instead of sharing sensitive data directly, each device trains a model using its own data and then shares only the results of that training with a central server. This way, the original data remains secure and private. The framework makes it easier for companies to collaborate on improving their technologies without compromising user privacy. By combining knowledge from many sources, the Flower Federated Framework aims to create better and more accurate AI models that benefit everyone.