Practice Federated Learning (FL) - 13.3 | 13. Privacy-Aware and Robust Machine Learning | Advance Machine Learning
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skillsβ€”perfect for learners of all ages.

games

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is one advantage of Federated Learning?

πŸ’‘ Hint: Think about where the data resides during training.

Question 2

Easy

What does the central server do in Federated Learning?

πŸ’‘ Hint: Consider what information gets shared.

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 does Federated Learning primarily help enhance?

  • Data exposure
  • Data privacy
  • Data centralization

πŸ’‘ Hint: Think about the primary goal of keeping user data secure.

Question 2

True or False: Federated Learning sends raw data to the central server.

  • True
  • False

πŸ’‘ Hint: Consider what is shared with the central server.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a Federated Learning system for a healthcare application that needs to comply with HIPAA regulations. Discuss how you would ensure data security and model integrity.

πŸ’‘ Hint: Think about both patient privacy and system security in your design.

Question 2

Evaluate the implications of non-IID data when training across multiple client devices in Federated Learning. How would you address this issue in practical scenarios?

πŸ’‘ Hint: Consider the different distributions of data that could exist among clients.

Challenge and get performance evaluation