How is federated learning different from traditional learning?

What is federated learning and how does it differ from traditional learning?

Federated learning is a decentralized approach where models are trained across multiple devices without sharing data. In contrast, traditional learning centralizes data on a single server.

Why is federated learning considered more privacy-friendly?

It enhances privacy by keeping data on local devices, reducing the risk of data breaches and ensuring user confidentiality.

Can you give an example of where federated learning is used?

Sure! It's commonly used in mobile devices for predictive text and keyboard suggestions, allowing models to improve without accessing personal data.

What are the challenges of implementing federated learning?

Some challenges include dealing with heterogeneous data, ensuring model convergence, and managing communication costs between devices.