Federated and Transfer Learning

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 privacy and incentive factors of federated learning. It is intended for students and academics, as well as scientists and practitioners in AI industries, autonomous vehicles, and cyber-physical systems.

Format: Hardback
Length: 371 pages
Publication date: 01 October 2022
Publisher: Springer International Publishing AG

This comprehensive book offers a treasure trove of cutting-edge research on the captivating realms of learning from decentralized data, seamlessly transferring information across domains, and tackling theoretical challenges related to enhancing privacy and incentive mechanisms in federated learning, as well as its interplay with transfer learning and reinforcement learning. In recent years, the machine learning community has been captivated by the fascinating world of federated and transfer learning. These approaches have achieved remarkable success and widespread popularity across a diverse array of fields. The primary target audience of this book encompasses students and scholars eager to harness the power of federated and transfer learning to address a wide range of real-world problems. It also appeals to scientists, researchers, and practitioners in the AI industry, autonomous vehicles, and cyber-physical systems who are eager to explore new scientific breakthroughs and stay abreast of the latest developments in federated and transfer learning and their practical applications.

The book is organized into five chapters, each dedicated to exploring different aspects of federated and transfer learning. In the first chapter, the authors provide an insightful introduction to the topic, highlighting its significance and potential applications. They discuss the challenges associated with traditional machine learning and introduce the concept of federated learning, which involves training models collaboratively across distributed datasets. The chapter also explores the benefits of federated learning, such as improved privacy, reduced data bias, and enhanced generalization.

Chapter 2 delves into the fundamentals of transfer learning, which involves leveraging knowledge learned from one task to improve performance on another. The authors discuss various transfer learning techniques, such as feature extraction, model adaptation, and transfer learning algorithms. They also explore the challenges of transfer learning, such as domain shift, data imbalance, and the lack of shared features.

Chapter 3 explores the role of privacy in federated learning, emphasizing the importance of protecting sensitive data while maintaining the accuracy of the models. The authors discuss various privacy-preserving techniques, such as differential privacy, encryption, and federated learning with noise. They also explore the trade-offs between privacy and utility and discuss the challenges of achieving both in practice.

Chapter 4 examines the incentive mechanisms in federated learning, focusing on designing effective rewards and penalties to motivate participants. The authors discuss various incentive mechanisms, such as reputation systems, monetary rewards, and game-theoretic approaches. They also explore the challenges of designing fair and effective incentive mechanisms, such as balancing the interests of different participants and ensuring transparency.

Chapter 5 explores the connection between federated learning and reinforcement learning, highlighting the similarities and differences between these two approaches. The authors discuss the challenges of training deep reinforcement learning models in federated settings and explore the potential benefits of combining federated learning and reinforcement learning. They also discuss the applications of federated and transfer learning in autonomous vehicles, cyber-physical systems, and other domains.

In conclusion, this book provides a comprehensive and up-to-date overview of federated and transfer learning, covering a wide range of topics and applications. It is an invaluable resource for students, scholars, and practitioners in the field, offering valuable insights and practical guidance on leveraging these powerful learning techniques to solve real-world problems.

Weight: 735g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031117473
Edition number: 1st ed. 2023