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.

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.

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.