Federated Learning For Smart Communication Using Iot Application
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| Author | : Kaushal Kishor |
| Publisher | : Chapman & Hall/CRC |
| Total Pages | : 0 |
| Release | : 2024-10-30 |
| Genre | : Computers |
| ISBN | : 9781032788128 |
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 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 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 anyone interested in federated learning-based intelligent algorithms for smart communications.
| Author | : Kaushal Kishor |
| Publisher | : CRC Press |
| Total Pages | : 275 |
| Release | : 2024-10-30 |
| Genre | : Computers |
| ISBN | : 1040146317 |
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.
| Author | : Yu-Dong Zhang |
| Publisher | : Springer Nature |
| Total Pages | : 752 |
| Release | : 2021-10-25 |
| Genre | : Technology & Engineering |
| ISBN | : 9811640165 |
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.
| Author | : Tomonobu Senjyu |
| Publisher | : Springer Nature |
| Total Pages | : 823 |
| Release | : 2023-07-21 |
| Genre | : Technology & Engineering |
| ISBN | : 9819907691 |
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.
| Author | : Satya Prakash Yadav |
| Publisher | : Springer Nature |
| Total Pages | : 269 |
| Release | : 2022-02-02 |
| Genre | : Technology & Engineering |
| ISBN | : 3030855597 |
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.
| Author | : Hassan, Ahdi |
| Publisher | : IGI Global |
| Total Pages | : 372 |
| Release | : 2024-02-14 |
| Genre | : Medical |
| ISBN | : |
The Healthcare sector is experiencing a mindset change with the advent of Healthcare 5.0, bringing forth improved patient care and system efficiency. However, this transformation poses significant challenges. The growing digitization of healthcare systems raises concerns about the security and privacy of patient data, making seamless data sharing and collaboration increasingly complex tasks. Additionally, as the volume of healthcare data expands exponentially, efficient handling and analysis become vital for optimizing healthcare delivery and patient outcomes. Addressing these multifaceted issues is crucial for healthcare professionals, IT experts, data scientists, and researchers seeking to fully harness the potential of Healthcare 5.0. Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security presents a comprehensive solution to the pressing challenges in the digitalized healthcare industry. This research book dives into the principles of Healthcare 5.0 and explores practical implementation through cloud computing, data analytics, and federated learning. Readers will gain profound insights into the role of cloud computing in managing vast amounts of healthcare data, such as electronic health records and real-time analytics. Cloud-based frameworks, architectures, and relevant use cases are explored to optimize healthcare delivery and improve patient outcomes.
| Author | : Jyoti Choudrie |
| Publisher | : Springer Nature |
| Total Pages | : 660 |
| Release | : 2023-08-30 |
| Genre | : Technology & Engineering |
| ISBN | : 9819937612 |
This book gathers papers addressing state-of-the-art research in all areas of information and communication technologies and their applications in intelligent computing, cloud storage, data mining and software analysis. It presents the outcomes of the Seventh International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2023), held in Ahmedabad, India. The book is divided into two volumes. It discusses the fundamentals of various data analysis techniques and algorithms, making it a valuable resource for researchers and practitioners alike.
| Author | : Qiang Yang |
| Publisher | : Springer Nature |
| Total Pages | : 291 |
| Release | : 2020-11-25 |
| Genre | : Computers |
| ISBN | : 3030630765 |
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
| Author | : Jayakrushna Sahoo |
| Publisher | : CRC Press |
| Total Pages | : 353 |
| Release | : 2024-09-20 |
| Genre | : Computers |
| ISBN | : 1040088597 |
This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how federated learning integrates AI technologies, such as blockchain, machine learning, IoT, edge computing, and fog computing systems, allowing multiple collaborators to build a robust machine-learning model using a large dataset. It highlights the capabilities and benefits of federated learning, addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. The volume first introduces the general concepts of machine learning and then summarizes the federated learning system setup and its associated terminologies. It also presents a basic classification of FL, the application of FL for various distributed computing scenarios, an integrated view of applications of software-defined networks, etc. The book also explores the role of federated learning in the Internet of Medical Things systems as well. The book provides a pragmatic analysis of strategies for developing a communication-efficient federated learning system. It also details the applicability of blockchain with federated learning on IoT-based systems. It provides an in-depth study of FL-based intrusion detection systems, discussing their taxonomy and functioning and showcasing their superiority over existing systems. The book is unique in that it evaluates the privacy and security aspects in federated learning. The volume presents a comprehensive analysis of some of the common challenges, proven threats, and attack strategies affecting FL systems. Special coverage on protected shot-based federated learning for facial expression recognition is also included. This comprehensive book, Federated Learning: Principles, Paradigms, and Applications, will enable research scholars, information technology professionals, and distributed computing engineers to understand various aspects of federated learning concepts and computational techniques for real-life implementation.
| Author | : Celestine Iwendi |
| Publisher | : Springer Nature |
| Total Pages | : 744 |
| Release | : 2023-10-25 |
| Genre | : Technology & Engineering |
| ISBN | : 303137164X |
Today, communication technology and computer engineering are intertwined, with advances in one field driving advances in the other, leading to the development of outstanding technologies. This book delves into the latest trends and breakthroughs in the areas of communication, Internet of things, cloud computing, big data, artificial intelligence, and machine learning. This book discusses challenges and opportunities that arise with the integration of communication technology and computer engineering. In addition, the book examines the ethical and social implications, including issues related to privacy, security, and digital divide and law. We have explored the future direction of these fields and the potential for further breakthroughs and innovations. The book is intended for a broad audience of undergraduate and graduate students, practicing engineers, and readers without a technical background who have an interest in learning about communication technology and computer engineering.
