Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows mul...Lasīt vairāk
This book offers a detailed exploration of how federated learning can address critical challenges in modern cybersecurity. It begins with an introduction to the core principles of federated learning. Then it highlights a strong foundation by explo...Lasīt vairāk
This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distributed comp...Lasīt vairāk
Balamurugan Balusamy, Daniel Arockiam, Pethuru Raj
Sērija : Computing and Networks
(Izdošanas datums: 04-Feb-2025, Hardback, Izdevniecība: Institution of Engineering and Technology, ISBN-13: 9781839539626)
Smart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particul...Lasīt vairāk
This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how f...Lasīt vairāk
(Izdošanas datums: 04-Sep-2024, Hardback, Izdevniecība: World Scientific Publishing Co Pte Ltd, ISBN-13: 9789811292545)
Authored by researchers and practitioners who build cutting-edge federated learning applications to solve real-world problems, this book covers the spectrum of federated learning technology from concepts and application scenarios to advanced algorit...Lasīt vairāk
This LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows:FLFM-WWW 2024, FLFM-ICME 2024, FLFM-IJCAI 2024 and FLFM-NeurIPS 2024. This LNAI volume focuses on the following topics:...Lasīt vairāk
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as...Lasīt vairāk
