Federated Learning: Theory and Practice provides a holistic treatment to federated learning, starting with a broad overview on federated learning as a distributed learning system with various forms of decentralized data and features. A detailed exposition then follows of core challenges and practical modeling techniques and solutions, spanning a variety of aspects in communication efficiency, theoretical convergence and security, viewed from different perspectives. Part II features emerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. To bridge the gap between academic and industrial research Part III presents a wide array of industrial applications of federated learning. Part IV concludes the book with several chapters highlighting potential venues and visions for federated learning in the near future.Federated Learning: Theory and Practice provides a comprehensive and accessible introduction to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavours