ISBN13: | 9781032788128 |
ISBN10: | 1032788127 |
Binding: | Hardback |
No. of pages: | 274 pages |
Size: | 234x156 mm |
Weight: | 666 g |
Language: | English |
Illustrations: | 42 Illustrations, black & white; 42 Line drawings, black & white; 7 Tables, black & white |
699 |
Combinatorics and graph theory
Electrical engineering and telecommunications, precision engineering
Theory of computing, computing in general
Mathematical Theory of computing
Computer architecture, logic design
Supercomputers
Operating systems and graphical user interfaces
Computer programming in general
Software development
High-level programming
Computer networks in general
Artificial Intelligence
Environmental sciences
Internet in general
Discrete mathematics
Combinatorics and graph theory (charity campaign)
Electrical engineering and telecommunications, precision engineering (charity campaign)
Theory of computing, computing in general (charity campaign)
Mathematical Theory of computing (charity campaign)
Computer architecture, logic design (charity campaign)
Supercomputers (charity campaign)
Operating systems and graphical user interfaces (charity campaign)
Computer programming in general (charity campaign)
Software development (charity campaign)
High-level programming (charity campaign)
Computer networks in general (charity campaign)
Artificial Intelligence (charity campaign)
Environmental sciences (charity campaign)
Internet in general (charity campaign)
Discrete mathematics (charity campaign)
Federated Learning for Smart Communication using IoT Application
GBP 145.00
Click here to subscribe.
Not in stock at Prospero.
The book aims to demonstrate the effectiveness of federated learning in high-performance information systems and informatics-based solutions for addressing current information support requirements.
The effectiveness of federated learning in high?performance information systems and informatics?based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT?based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.
Features:
- Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users? privacy
- Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy
- Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area
- Analyses the need for a personalized federated learning framework in cloud?edge and wireless?edge architecture for intelligent IoT applications
- Comprises real?life case illustrations and examples to help consolidate understanding of topics presented in each chapter
This book is recommended for anyone interested in federated learning?based intelligent algorithms for smart communications.
1. Introduction to Federated Learning: Transforming Collaborative Machine Learning for a Decentralized Future 2. Applications, Challenges, and Opportunities for Federated Learning in 6G 3. Unleash Federated Machine Learning and Internet of Medical Things (IoMT) for Diseases Screening and Enhancement of Smart Healthcare 4. Federated Machine Learning in Medical Science: A Perspective Investigation 5. Artificial Intelligence Techniques Based on Federated Learning in Smart Healthcare 6. Federated Machine Learning in Medical Science: A Prospective Investigation 7. Healthcare Informatics Security Issues and Solutions using Federated Learning 8. Innovative Solutions: Exploring Federated Learning-Based Resource Virtualization with AR Integration in Healthcare Environments 9. Securing the Connected World: Federated Learning and IoT Cybersecurity 10. Federated Learning Shaping the Future of Smart City Infrastructure 11. EmPowering Teaching Institutes: Integrating Federated Learning in the Internet of Things (IOT) 12. A Critical Role for Federated Learning in IoT