FEDERATED LEARNING: FROM ALGORITHMS TO SYSTEM IMPLEMENTATION

LIEFENG BO, HENG HUANG SONGXIANG GU YA (Author)

ISBN 10: 981129254X / ISBN 13: 9789811292545

Verlag: World Scientific, 2024

Beschreibung

527 pages. 9.25x6.25x1.50 inches. In Stock. Bestandsnummer des Verkäufers x-981129254X

Bibliografische Details

Titel: FEDERATED LEARNING: FROM ALGORITHMS TO ...

Verlag: World Scientific

Erscheinungsdatum: 2024

Einband: Hardcover

Zustand: Brand New

Über diesen Titel

Inhaltsangabe:

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 algorithms and finally system implementation in three parts. It provides a comprehensive review and summary of federated learning technology, as well as presenting numerous novel federated learning algorithms which no other books have summarized. The work also references the most recent papers, articles and reviews from the past several years to keep pace with the academic and industrial state of the art of federated learning.

The first part lays a foundational understanding of federated learning by going through its definition and characteristics, and also possible application scenarios and related privacy protection technologies. The second part elaborates on some of the federated learning algorithms innovated by JD Technology which encompass both vertical and horizontal scenarios, including vertical federated tree models, linear regression, kernel learning, asynchronous methods, deep learning, homomorphic encryption, and reinforcement learning. The third and final part shifts in scope to federated learning systems - namely JD Technology's own FedLearn system - by discussing its design and implementation using gRPC, in addition to specific performance optimization techniques plus integration with blockchain technology.

This book will serve as a great reference for readers who are experienced in federated learning algorithms, building privacy-preserving machine learning applications or solving real-world problems with privacy-restricted scenarios.