Học liên kết: Lý thuyết và Thực hành

"Federated Learning: Theory and Practical" is designed to provide you with a comprehensive introduction to one of the most exciting and evolving areas in machine learning—federated learning (FL). In an era where data privacy is becoming increasingly important, FL offers a solution by enabling machine learning models to be trained across decentralized data sources, such as smartphones or local clients, without the need to share sensitive data.

This course starts with the basics of machine learning to ensure a solid foundation. You will then dive into the core concepts of federated learning, including the motivations behind its development, the different types (horizontal, vertical, and combined FL), and how it compares to traditional machine learning approaches.

By week three, you'll not only grasp the theory but also be ready to implement FL systems from scratch and using popular frameworks like FLOWER. You’ll explore advanced topics such as privacy-enhancing technologies, including differential privacy and homomorphic encryption, and gain insight into practical challenges like client selection and gradient inversion attacks.

Whether you are a data scientist, machine learning engineer, or someone curious about privacy-preserving AI, this course offers the theoretical grounding and hands-on skills necessary to navigate the emerging landscape of federated learning.