Practice Overview - 13.3.1 | 13. Privacy-Aware and Robust Machine Learning | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is Federated Learning?

πŸ’‘ Hint: Think about where the data is stored.

Question 2

Easy

Name one benefit of Federated Learning.

πŸ’‘ Hint: How does it affect data security?

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is Federated Learning?

  • A centralized model training approach.
  • A decentralized approach focusing on privacy.
  • An unsupervised learning technique.

πŸ’‘ Hint: Remember how it utilizes local data.

Question 2

True or False: Federated Learning requires sending raw data to a central server.

  • True
  • False

πŸ’‘ Hint: Think about the core principle of data locality.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Evaluate the potential impact of using Federated Learning in a healthcare app designed for individualized treatment plans while considering privacy issues.

πŸ’‘ Hint: Focus on how local data processing benefits patient privacy.

Question 2

Propose strategies to mitigate communication overhead in Federated Learning systems.

πŸ’‘ Hint: Think about how to manage the data sent between clients and the server.

Challenge and get performance evaluation