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This book provides a collection of recent research works on
learning from decentralized data, transferring information from one
domain to another, and addressing theoretical issues on improving
the privacy and incentive factors of federated learning as well as
its connection with transfer learning and reinforcement learning.
Over the last few years, the machine learning community has become
fascinated by federated and transfer learning. Transfer and
federated learning have achieved great success and popularity in
many different fields of application. The intended audience of this
book is students and academics aiming to apply federated and
transfer learning to solve different kinds of real-world problems,
as well as scientists, researchers, and practitioners in AI
industries, autonomous vehicles, and cyber-physical systems who
wish to pursue new scientific innovations and update their
knowledge on federated and transfer learning and their
applications.
This book provides a collection of recent research works addressing
theoretical issues on improving the learning process and the
generalization of GANs as well as state-of-the-art applications of
GANs to various domains of real life. Adversarial learning
fascinates the attention of machine learning communities across the
world in recent years. Generative adversarial networks (GANs), as
the main method of adversarial learning, achieve great success and
popularity by exploiting a minimax learning concept, in which two
networks compete with each other during the learning process. Their
key capability is to generate new data and replicate available data
distributions, which are needed in many practical applications,
particularly in computer vision and signal processing. The book is
intended for academics, practitioners, and research students in
artificial intelligence looking to stay up to date with the latest
advancements on GANs' theoretical developments and their
applications.
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