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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 (FL) as well as its connection with transfer learning and Reinforcement Learning. Over the last few years, the Machine Learning (ML) 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 aims to serve two related goals. First, the book provides high-level background information that will allow students, researchers, and practitioners to quickly get up to speed in these exciting areas, understanding what has been done, how the algorithms work, how they are related, and what are some of the important open problems. Second, the book showcases novel contributions over state of the art, providing significant contributions to the field. We hope that these individual contributions can not only be used directly, but also serve as starting points for completely novel research.
An Introduction to Federated and Transfer Learning
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
Cross-Silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems
A Unifying Framework for Federated Learning
A Contract Theory Based Incentive Mechanism for Federated Learning
A Study of Blockchain-Based Federated Learning
Swarm Meta Learning
Rethinking Importance Weighting for Transfer Learning
Transfer Learning via Representation Learning
Modeling Individual Humans via a Secondary Task Transfer Learning Method
From Theoretical to Practical Transfer Learning: The ADAPT Library
Lyapunov Robust Constrained-MDPs for Sim2Real Transfer Learning
A Study on Efficient Reinforcement Learning Through Knowledge Transfer
Federated Transfer Reinforcement Learning for Autonomous Driving