Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications

Synopsis

Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problemsGenerating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablersCompressing AI models so that computational, memory, storage, and network requirements can be substantially reducedAddressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous dataOvercoming cyberattacks on mission-critical software systems by leveraging federated learning Written in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders.

Copyright:

2025

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9781394219223

Related ISBNs:

9781394219216, 9781394219230

Publisher:

Wiley

Date of Addition:

11/14/24

Copyrighted By:

The Institute of Electrical and Electronics Engineers, Inc.

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Computers and Internet, Mathematics and Statistics

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.

Edited by:

Pethuru Raj Chelliah

Edited by:

Amir Masoud Rahmani

Edited by:

Robert Colby

Edited by:

Gayathri Nagasubramanian

Edited by:

Sunku Ranganath