What are the applications of secure aggregation in AI?

What are the applications of secure aggregation in AI?

Secure aggregation is used in AI to protect sensitive data while enabling collaborative learning, like federated learning.

Can you explain how it works in federated learning?

In federated learning, data remains on local devices, and only aggregated updates are shared, ensuring privacy.

What are some real-world examples of this technology?

Real-world examples include healthcare data analysis and mobile keyboard predictions, where user data privacy is crucial.

Are there any challenges associated with secure aggregation?

Yes, challenges include ensuring data integrity, managing communication overhead, and dealing with malicious participants.