Federated Learning: A Comprehensive Overview of Methods and Applications

Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. This book provides an in-depth discussion of the most important issues and approaches to FL for researchers and practitioners,covering algorithmic questions,privacy and security,distributed systems,and specific application domains. It also covers other important use cases for FL,such as split learning and vertical federated learning,and applies FL in real-world enterprise settings.

Format: Hardback
Length: 534 pages
Publication date: 08 July 2022
Publisher: Springer Nature Switzerland AG

Federated Learning (FL) is a revolutionary approach to machine learning that enables distributed training of models across multiple data parties without the need for centralized data management. This approach is gaining popularity due to its ability to address privacy concerns, regulatory requirements, and practical challenges associated with centralized data storage. In this comprehensive overview, we will delve into the core concepts, methods, and applications of Federated Learning.

The first part of the book will provide a foundational understanding of Federated Learning. We will explore the basic principles, challenges, and advantages of this approach. We will discuss the core machine learning perspective, privacy and security considerations, distributed systems, and specific application domains where Federated Learning finds applications.

The second part of the book will focus on the algorithmic aspects of Federated Learning. We will explore different techniques and methodologies for solving machine learning tasks in a federated setting. We will discuss the challenges of training models efficiently, at scale, and with fairness across different data parties. We will also explore the use of optimization algorithms, such as stochastic gradient descent, to train models collaboratively.

The third part of the book will address the practical considerations of implementing Federated Learning systems. We will discuss the design and architecture of federated learning systems, including the choice of communication protocols, data distribution strategies, and model evaluation metrics. We will also explore the challenges of deploying federated learning systems in real-world scenarios and address the scalability and performance issues associated with this approach.

The fourth part of the book will focus on specific application domains where Federated Learning has demonstrated its effectiveness. We will explore applications in healthcare, finance, social media, and other fields. We will discuss the use of Federated Learning to address privacy concerns, improve model accuracy, and enable personalized learning.

In conclusion, Federated Learning is a transformative approach to machine learning that has the potential to revolutionize the way we develop and deploy intelligent systems. This comprehensive overview provides a deep understanding of the core concepts, methods, and applications of Federated Learning, enabling researchers and practitioners to leverage its benefits in a wide range of domains.

Weight: 975g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030968953
Edition number: 1st ed. 2022