Federated Learning Tutorial: Concepts, Applications, Challenges, and Framework

Explore the world of federated learning in this comprehensive tutorial that delves into its concepts, applications, challenges, and frameworks. Learn about the innovative approach to machine learning that allows for training models on distributed datasets without compromising data privacy. Discover how federated learning is revolutionizing various industries, from healthcare to finance, by enabling collaborative model training across multiple parties. Understand the technical challenges faced in implementing federated learning systems and explore solutions to overcome them. Gain insights into popular federated learning frameworks and their practical applications. Whether you're a data scientist, machine learning engineer, or privacy enthusiast, this 59-minute tutorial by Zilinghan Li from MICDE University of Michigan provides a solid foundation for understanding and implementing federated learning in real-world scenarios.

Zilinghan Li: Federated Learning Tutorial: Concepts, Applications, Challenges, and Framework