The book Security of Internet of Things Nodes: Challenges, Attacks, and Countermeasures (R) covers a wide range of research topics on the security of the Internet of Things nodes along with the latest research development in the domain of Internet of Things. It also covers various algorithms, techniques, and schemes in the field of computer science with state-of-the-art tools and technologies. This book mainly focuses on the security challenges of the Internet of Things devices and the countermeasures to overcome security vulnerabilities. Also, it highlights trust management issues on the Internet of Things nodes to build secured Internet of Things systems. The book also covers the necessity of a system model for the Internet of Things devices to ensure security at the hardware level.

This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors' control of their critical data.

This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors' control of their critical data.