What are the differences between federated learning and centralized learning?

What are the differences between federated learning and centralized learning?

Federated learning allows models to be trained across multiple devices without sharing data, while centralized learning involves gathering all data in one location for training.

What are the advantages of federated learning?

Federated learning enhances privacy, reduces latency, and allows for training on diverse data sources without compromising user data.

Are there any disadvantages to federated learning?

Yes, federated learning can face challenges like communication overhead, model convergence issues, and the need for robust device management.

In what scenarios is centralized learning preferred?

Centralized learning is preferred when data is homogeneous, when high computational power is available, or when data privacy is less of a concern.

Can you give examples of applications for both learning types?

Sure! Federated learning is used in mobile keyboard predictions, while centralized learning is common in traditional data analytics and cloud-based applications.