1st Edition

Federated AI for Real-World Business Scenarios

  • Available for pre-order. Item will ship after October 1, 2021
ISBN 9780367861575
October 1, 2021 Forthcoming by CRC Press
218 Pages 93 B/W Illustrations

USD $165.00

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Book Description

This book provides an overview of Federated Learning and how it can be used to build real-world AI-enabled applications. Real-world AI applications frequently have training data distributed in many different locations, with data at different sites having different properties and different formats. In many cases data movement is not permitted due to concerns arising out of security, bandwidth, cost or regulatory restriction. Under these conditions, techniques of federated learning can enable creation of practical applications. Creating practical applications requires a complete implementation of the cycle of learning from data, inferring from data, and acting based on the inference. This book will be the first one to cover all stages of the Learn-Infer-Act cycle, and presents a set of patterns to apply federation to all stages. Another distinct feature of the book is the use of real-world applications with an approach that discusses all aspects that need to be considered in an operational system, including handling of data issues during federation, maintaining compliance with enterprise security policies, and simplifying the logistics of federated AI in enterprise contexts. The book considers federation from a in a manner agnostic to the actual AI model, allowing the concepts to be applied to all varities of AI model. This book is probably the first one to cover the space of enterprise AI based applications in a holistic manner.

Table of Contents

Introduction to Artificial Intelligence. Scenarios for Federated AI. Naive Federated Learning Approaches. Addressing Data Mismatch Issues in Federated AI. Addressing Data Skew Issues in Federated Learning. Addressing Trust Issues in Federated Learning. Addressing Synchronization Issues in Federated Learning. Addressing Vertical Partitioning Issues in Federated Learning. Use Cases. References.

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Dinesh C. Verma is a Researcher and Department Group Manager of the Distributed AI area at IBM T. J. Watson Research Center, Yorktown Heights, New York. He has 25+ years of professional experience after his Ph.D. in Computer Science from University of California Berkeley. He holds a bachelor’s degree in Computer Science from Indian Institute of Technology, Kanpur, India (President Gold Medalist), and Masters in Management of Technology from NYU.

Dinesh has authored 11 books, 120+ technical papers and been granted 135+ patents. He has chaired various IEEE technical committees, served on multiple conference executive & technical committees, editorial boards and managed large international multi-institutional government programs. He is a member of the IBM Academy of Technology, an IBM Master Inventor and has won several IBM technical outstanding achievement awards, including designation as an IBM Fellow, the highest technical recognition within the company. He is a Fellow of Royal Academy of Engineering and an IEEE Fellow.

Dinesh is responsible for defining the IBM strategy in the area of edge computing and AI enablement for Internet of Things. He is the Principal Investigator for the US - UK International Technology Alliance, a group of 15 academic, industrial and government research organizations in U.S. and UK looking at the challenges of creating AI tasks in a federated multi-organization environment.