Home » Federated AI for Real-World Business Scenarios
Product Image

Federated AI for Real-World Business Scenarios

Dinesh C. Verma
€198.40
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
A01=Dinesh C. Verma
AI enabled Multi Domain Operations
AI for Consortiums
Ai Model
Author_Dinesh C. Verma
Business Process
Category=UY
Category=UYQ
Category=UYQN
Cloud Service
Cloud Service Provider
data heterogeneity challenges
Data Set
Decision Table
distributed machine learning
Edge Sites
Enterprise AI
enterprise data governance
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Federated Averaging
Federated Inference
Federated Learning
federated learning implementation patterns
Function Estimation
Fusion AI
Fusion Server
Fusion Sites
GDPR
Ground Truth
Homomorphic Encryption
Input Features
Intermediary Output
Machine Learning in Enterprises
Neural Networks
operational ai systems
privacy preserving ai
Proxy Sites
secure collaborative analytics
SVM
Training Data
Trust Zones
Vertical Partitioning
Yellow Zone

Product details

  • ISBN 9780367861575
  • Weight: 460g
  • Dimensions: 156 x 234mm
  • Publication Date: 01 Oct 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
Secure checkout Fast Shipping Easy returns
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 security concerns, bandwidth, cost or regulatory restriction. Under these conditions, techniques of federated learning can enable creation of practical applications. Creating practical applications requires 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 manner agnostic to the actual AI models, allowing the concepts to be applied to all varieties of AI models. This book is probably the first one to cover the space of enterprise AI-based applications in a holistic manner.
About Dinesh C. Verma

Dinesh C. Verma is an IBM Fellow, a UK Fellow of the Royal Academy of Engineering and an IEEE Fellow. He leads the Distributed AI area at IBM Watson Research Center. He has authored ten books, 150+ technical papers and been granted 185+ U.S. patents. He has led an international consortium of scientists for fifteen years, and supervised many business solutions using AI. More details about Dinesh are available at ibm.biz/dineshverma

More from this author