Federated Learning: Theory and Practice

provides a holistic treatment to federated learning as a distributed learning system with various forms of decentralized data and features.

Part I of the book begins with a broad overview of optimization fundamentals and modeling challenges, covering various aspects of communication efficiency, theoretical convergence, and security.

Part II features emerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service.

Part III concludes the book with a wide array of industrial applications of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data.

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 advances in machine learning for their entrepreneurial endeavors.

- Presents the fundamentals and a survey of key developments in the field of federated learning

- Provides emerging, state-of-the-art topics that build on fundamentals

- Contains industry applications

- Gives an overview of visions of the future