What is federated learning architecture?

What is federated learning architecture?

Federated learning architecture is a decentralized approach to machine learning where multiple devices collaboratively train a model while keeping their data local.

How does it ensure data privacy?

It ensures data privacy by allowing devices to train the model on their local data and only share model updates, not the data itself.

What are the main benefits of using federated learning?

The main benefits include enhanced data privacy, reduced latency, and the ability to leverage data from multiple sources without centralizing it.

Can you give an example of its application?

Sure! One common application is in mobile devices, where federated learning can improve predictive text models without sending user data to the cloud.