There are over 5 billion mobile device users all over the world. These users generate massive amounts of data—through cameras, microphones, and sensors like accelerometers—which can be leveraged for building intelligent applications.
So, what is federated learning?
Federated learning is a method for training AI models directly on users’ devices, without moving the raw data to a central server.
The model learns locally, sends only updates back to the cloud, and keeps your personal data private.
Traditionally, data is collected in centralised data centres to train machine learning and deep learning models.
Continue reading “Federated Learning: A Practical Guide to Decentralised Machine Learning”