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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
Adversarial Robustness for Machine Learning summarizes the recent
progress on this topic and introduces popular algorithms on
adversarial attack, defense and veri?cation. Sections cover
adversarial attack, veri?cation and defense, mainly focusing on
image classi?cation applications which are the standard benchmark
considered in the adversarial robustness community. Other sections
discuss adversarial examples beyond image classification, other
threat models beyond testing time attack, and applications on
adversarial robustness. For researchers, this book provides a
thorough literature review that summarizes latest progress in the
area, which can be a good reference for conducting future research.
In addition, the book can also be used as a textbook for graduate
courses on adversarial robustness or trustworthy machine learning.
While machine learning (ML) algorithms have achieved remarkable
performance in many applications, recent studies have demonstrated
their lack of robustness against adversarial disturbance. The lack
of robustness brings security concerns in ML models for real
applications such as self-driving cars, robotics controls and
healthcare systems.
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