Federated AI for Real-World Business Scenarios

This book provides an overview of Federated Learning and how it can be used to build real-world AI-enabled applications. It covers the cycle of learning from data, inferring from data, and acting based on the inference, and uses real-world applications with an approach that discusses all aspects that need to be considered in an operational system. It is the first book to cover the space of enterprise AI-based applications in a holistic manner.

Format: Paperback / softback
Length: 206 pages
Publication date: 29 January 2024
Publisher: Taylor & Francis Ltd

This comprehensive book delves into the realm of Federated Learning, offering a comprehensive overview of its applications in building real-world AI-enabled solutions. In the realm of AI, real-world applications often encounter the challenge of distributed training data, located in various locations with diverse properties and formats. Due to security concerns, bandwidth limitations, cost constraints, or regulatory restrictions, data movement may not be feasible. However, federated learning techniques provide a solution to this problem by enabling the creation of practical applications in such scenarios.

The process of federated learning involves a cycle of learning from data, inferring from data, and acting based on the inference. This book is unique in its coverage of all stages of this cycle, providing a set of patterns that can be applied to each stage. Furthermore, the book emphasizes the practical aspects of federated learning, discussing considerations such as data handling during federation, maintaining compliance with enterprise security policies, and simplifying the logistics of federated AI in enterprise contexts.

One of the distinguishing features of this book is its agnostic approach to federated learning, which allows the concepts to be applied to a wide range of AI models. It does not focus solely on specific AI algorithms or frameworks, making it accessible to a broad audience of practitioners and researchers in the field.

Moreover, this book takes a holistic view of enterprise AI-based applications, covering various aspects that are crucial for successful implementation. It discusses the challenges and opportunities associated with federated learning in enterprise environments, including data privacy, scalability, and integration with existing systems.

By providing a comprehensive and practical guide to federated learning, this book is an invaluable resource for anyone seeking to develop AI-enabled solutions that can operate in the real world, despite the challenges posed by distributed data and heterogeneous environments. Whether you are a practitioner, researcher, or business leader, this book will help you navigate the complexities of federated learning and unlock the full potential of AI in your applications.

Weight: 348g
Dimension: 156 x 233 x 15 (mm)
ISBN-13: 9781032049359