CyberCertPrepAI Security FundamentalsData Security & Privacy in AIEASYFree question
Scenario
A mobile OS company wants to improve its keyboard autocomplete model using the typing behavior of 200 million active users. A privacy engineer objects to collecting all keystrokes to a central server, noting the sensitivity of typed data (messages, passwords, health queries). She proposes an alternative training architecture that keeps the data on users' phones.
A data scientist proposes using federated learning for training a mobile keyboard autocomplete model. What is the primary privacy benefit of federated learning compared to centralized training?
- A.Federated learning keeps raw user data on-device; only model updates (gradients) are sent to the server, reducing data centralization
- B.Federated learning uses differential privacy automatically, providing mathematical privacy guarantees
- C.Federated learning encrypts model weights so they cannot be inspected by users; most MLOps platforms enforce this by default
- D.Federated learning trains on synthetic data, so real user data is never involved
Why A is correct