Model Optimization Methods for Efficient and Edge AI

Federated Learning Architectures, Frameworks and Applications

Pethuru Raj Chelliah, Amir Masoud Rahmani, Robert Colby, Gayathri Nagasubramanian, Sunku Ranganath

• Publisher: Wiley-IEEE Press
• Year: 2024
• Print ISBN: 9781394219216
• eBook ISBN: 9781394219209
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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 problems
• Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers
• Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced
• Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data
• Overcoming 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.

Table of contents

• Cover
• Title Page
• Copyright
• Contents
• About the Editors
• List of Contributors
• Chapter 1 Fundamentals of Edge AI and Federated Learning
• Chapter 2 AI Applications – Computer Vision and Natural Language Processing
• Chapter 3 An Overview of AI Platforms, Frameworks, Libraries, and Processors
• Chapter 4 Model Optimization Techniques for Edge Devices
• Chapter 5 AI Model Optimization Techniques
• Chapter 6 Federated Learning: Introduction, Evolution, Working, Advantages, and Its Application in Various Domains
• Chapter 7 Application Domains of Federated Learning
• Chapter 8 Advanced Architectures and Innovative Platforms for Federated Learning: A Comprehensive Exploration
• Chapter 9 Federated Learning: Bridging Data Privacy and AI Advancements
• Chapter 10 Securing Edge Learning: The Convergence of Block Chain and Edge Intelligence
• Chapter 11 Training on Edge
• Chapter 12 Architectural Patterns for the Design of Federated Learning Systems
• Chapter 13 Federated Learning for Intelligent IoT Systems: Background, Frameworks, and Optimization Techniques
• Chapter 14 Enhancing Cybersecurity Through Federated Learning: A Critical Evaluation of Strategies and Implications
• Chapter 15 Blockchain for Securing Federated Learning Systems: Enhancing Privacy and Trust
• Chapter 16 Blockchain‐Enabled Secure Federated Learning Systems for Advancing Privacy and Trust in Decentralized AI
• Chapter 17 An Edge Artificial Intelligence Federated Recommender System for Virtual Classrooms
• Chapter 18 Federated Learning in Smart Cities
• Index
• EULA