As Internet users attach importance to their own privacy, and a number of laws and regulations go into effect in most countries, Internet products need to provide users with privacy protection. As one of the feasible solutions to provide such privacy protection, federated learning has rapidly gained popularity in both academia and industry in recent years. In this tutorial, we will start off with some real-world tasks to illustrate the topic of federated learning, and cover some basic concepts and important scenarios including cross-device and cross-silo settings. Along with it, we will give several demonstrations with popular federated learning frameworks. We will also show how to do the automatic hyperparameter tuning with federated learning to significantly save their efforts in practice. Then we dive into three parallel hot topics, Personalized Federated Learning, Federated Graph Learning, and Attack in Federated Learning. For each of them, we will motivate it with real-world applications, illustrate the state-of-the-art methods, and discuss their pros and cons using concrete examples. As the last part, we will point out some future research directions.
