Federated Learning For Smart Communication Using Iot Application

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. To address heterogeneity challenges in IoT contexts, it 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 demonstrate the efficacy of personalized federated learning for intelligent IoT applications. - 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 Internet of Things (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 anybody interested in Federated Learning-based Intelligent Algorithms for Smart Communications.

This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering.

Federated Learning for Future Intelligent Wireless Networks Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. Readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues apply to wireless communications. Readers will also find: A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL Comprehensive explorations of wireless communication network design and optimization for federated learning Practical discussions of novel federated learning algorithms and frameworks for future wireless networks Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution Perfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.

In recent years, mobile technology and the internet of objects have been used in mobile networks to meet new technical demands. Emerging needs have centered on data storage, computation, and low latency management in potentially smart cities, transport, smart grids, and a wide number of sustainable environments. Federated learning’s contributions include an effective framework to improve network security in heterogeneous industrial internet of things (IIoT) environments. Demystifying Federated Learning for Blockchain and Industrial Internet of Things rediscovers, redefines, and reestablishes the most recent applications of federated learning using blockchain and IIoT to optimize data for next-generation networks. It provides insights to readers in a way of inculcating the theme that shapes the next generation of secure communication. Covering topics such as smart agriculture, object identification, and educational big data, this premier reference source is an essential resource for computer scientists, programmers, government officials, business leaders and managers, students and faculty of higher education, researchers, and academicians.

This book gathers high-quality papers presented at the Fifth International Conference on Smart Trends in Computing and Communications (SmartCom 2021), organized by Global Knowledge Research Foundation (GR Foundation) from March 2 – 3 , 2021. It covers the state of the art and emerging topics in information, computer communications, and effective strategies for their use in engineering and managerial applications. It also explores and discusses the latest technological advances in, and future directions for, information and knowledge computing and its applications.

This book gathers high-quality papers presented at the Seventh International Conference on Smart Trends in Computing and Communications (SmartCom 2022), organized by Global Knowledge Research Foundation (GR Foundation) from January 24–25, 2023, in Jaipur, India. It covers the state-of-the-art and emerging topics in information, computer communications, and effective strategies for their use in engineering and managerial applications. It also explores and discusses the latest technological advances in, and future directions for, information and knowledge computing and its applications.

Nowadays, all of us are connected through a large number of sensor nodes, smart devices, and wireless terminals. For these Internet of Things (IoT) devices to operate seamlessly, the Wireless Sensor Network (WSN) needs to be robust to support huge volumes of data for information exchange, resource optimization, and energy efficiency. This book provides in-depth information about the emerging paradigms of IoT and WSN in new communication scenarios for energy-efficient and reliable information exchange between a large number of sensor nodes and applications. WSN and IoT: An Integrated Approach for Smart Applications discusses how the integration of IoT and WSN enables an efficient communication flow between sensor nodes and wireless terminals and covers the role of machine learning (ML), artificial intelligence (AI), deep learning (DL), and blockchain technologies which give way to intelligent networks. This book presents how technological advancement is beneficial for real-time applications involving a massive number of devices and discusses how the network carries huge amounts of data allowing information to be communicated over the Internet. Intelligent transportation involving connected vehicles and roadside units is highlighted to show how a reality created through the intelligent integration of IoT and WSN is possible. Convergence is discussed and its use in smart healthcare, where only through the intelligent connection of devices can patients be treated or monitored remotely for telemedicine or telesurgery applications. This book also looks at how sustainable development is achieved by the resource control mechanism enabling energy-efficient communication. A wide range of communication paradigms related to smart cities, which includes smart healthcare, smart transportation, smart homes, and intelligent data processing, are covered in the book. It is aimed at academicians, researchers, advanced-level students, and engineers who are interested in the advancements of IoT and WSN for various applications in smart cities.