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
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