What is privacy in federated learning?

What is privacy in federated learning?

Privacy in federated learning refers to the protection of user data while training machine learning models across decentralized devices.

How does federated learning ensure data privacy?

Federated learning ensures data privacy by keeping the data on local devices and only sharing model updates, not raw data.

What are the benefits of using federated learning for privacy?

The benefits include reduced risk of data breaches, compliance with privacy regulations, and improved user trust.

Are there any challenges to maintaining privacy in federated learning?

Yes, challenges include potential model inversion attacks and ensuring secure communication between devices.

Can federated learning be used in healthcare?

Absolutely! Federated learning is particularly useful in healthcare for training models on sensitive patient data without compromising privacy.