Federated Learning is a machine learning approach that lets users collaborate to train models without sharing their data. By keeping data on individual devices, FL protects privacy while still enabling the benefits of collective training.

Over the past decade, FL has become a key part of many real-world systems, quietly running behind the scenes on millions of devices. Big companies like Google and Apple have used this technology to deliver smarter and more personalized experiences. Open-source tools like Flower (https://flower.ai) have also made it easier to experiment with FL.

Join me in this talk to discover the basics of Federated Learning, explore its real-world applications, and learn how to create a simple simulation of decentralized training using Flower.