CDPSE Privacy Engineering Practice Question
In a federated learning architecture, how is the model trained without exposing the underlying local datasets?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
By sending locally computed gradients to a central server
Federated learning works by sending the global model to local nodes, training it locally, and sending only the parameter updates (gradients) back to the central server.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
By using homomorphic encryption for every data point
Why it's wrong here
While it can be combined, it is not the defining feature of the federated learning mechanism itself.
- ✗
By moving all datasets to a centralized data lake
Why it's wrong here
Centralization is the opposite of the federated learning privacy model.
- ✗
By anonymizing the data using k-anonymity
Why it's wrong here
k-anonymity is not a requirement for the mechanics of federated learning.
- ✓
By sending locally computed gradients to a central server
Why this is correct
The raw data never leaves the local device; only the model updates are shared.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official ISACA exam blueprint
This CDPSE practice question is part of Courseiva's free ISACA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CDPSE exam.