Applications of Federated Learning in Technological Advancements
Use Cases and Applications
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 1 September 2025
- ISBN 9781032859514
- Binding Hardback
- No. of pages186 pages
- Size 234x156 mm
- Weight 500 g
- Language English
- Illustrations 33 Illustrations, black & white; 1 Halftones, black & white; 32 Line drawings, black & white; 15 Tables, black & white 700
Categories
Short description:
This book explores the applications and advancements of Federated Learning across diverse sectors, focusing on its integration with cutting-edge technologies like IoT, AI, Blockchain, and Digital Twins.
MoreLong description:
This book explores the applications and advancements of federated learning across diverse sectors, focusing on its integration with cutting- edge technologies like Internet of Things (IoT), artificial intelligence (AI), blockchain, and digital twins. Real-world examples and case studies illustrate federated learning’s role in healthcare, smart cities, and maritime applications while addressing critical concerns such as security. It provides insights into federated learning’s transformative potential, offering practical strategies for intelligent systems and sustainable environments.
The book particularly:
- Focuses on the federated learning–based model optimization, addressing the significance of IoT and federated learning in the evolution of intelligent systems for various applications
- Describes the different optimization techniques of federated learning systems from a practical point of view
- Highlights economic, social, and environmental impacts of smart technologies and provides insights into IoT, 5G/ 6G communication, and computing standards
- Provides analysis of the use cases of federated learning regarding the development of IoT, AI, blockchain, digital twins
- Offers strategies for overcoming challenges associated with federated learning systems, including connectivity, computation, threats, privacy, and security issues
It covers fundamental concepts, practical implementations, and trends, to serve as a reference resource for professionals and researchers in the field.
MoreTable of Contents:
1. Journey Towards Federated Learning: Fundamentals, Tools Paradigms, Opportunities and Challenges 2. Federated Learning-based algorithms for deployment and model optimization 3. Automation of AI and IoT-based Data-driven Decision-Making Approaches using Federated Learning Systems 4. Federated Learning for sustainable development using IoT/Edge Computing Systems 5. Advances in 5G/6G enabled federated reinforcement learning in IoT 6. Blockchain Integrated Federated Learning for IoT-based Smart Applications 7. Federated Learning in Heterogeneous Unmanned Aerial Vehicle 8. Advanced Technologies for Federated learning in Smart Cities and its use cases 9.Federated Deep Learning for Cyber-Physical Systems in Real-World Scenarios 10. Use-Cases and Scenarios for Federated Learning Adoption in IoT.
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