Federated Learning for IoT Applications
Federated Learning for IoT Applications

Author: Satya Prakash Yadav

Publisher: Springer Nature

Published: 2022-02-02

Total Pages: 269

ISBN-13: 3030855597

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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 Systems
Federated Learning Systems

Author: Muhammad Habib ur Rehman

Publisher: Springer Nature

Published: 2021-06-11

Total Pages: 207

ISBN-13: 3030706044

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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.

Federated Learning for Smart Communication Using IoT Application
Federated Learning for Smart Communication Using IoT Application

Author: Kaushal Kishor

Publisher:

Published: 2024-10-30

Total Pages: 0

ISBN-13: 9781032788128

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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.

2021 10th Mediterranean Conference on Embedded Computing (MECO)
2021 10th Mediterranean Conference on Embedded Computing (MECO)

Author: IEEE Staff

Publisher:

Published: 2021-06-07

Total Pages:

ISBN-13: 9781665429894

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Topics of interest include, but are not limited to Software and Hardware Architectures for Embedded Systems Systems on Chip (SoCs) and Multicore Systems Communications, Networking and Connectivity Sensors and Sensor Networks Mobile and Pervasive Ubiquitous Computing Distributed Embedded Computing Real Time Systems Adaptive Systems Reconfigurable Systems Design Methodology and Tools Application Analysis and Parallelization System Architecture Synthesis Multi objective Optimization Low power Design and Energy, Management Hardware Software Simulation Rapid prototyping Testing and Benchmarking Micro and Nano Technology Organic Flexible Printed Electronics MEMS VLSI Design and Implementation Microcontroller and FPGA Implementation Embedded Real Time Operating Systems Cloud Computing in Embedded System Development Digital Filter Design Digital Signal Processing and Applications Image and Multidimensional Signal Processing Embedded Systems in Multimedia, Related fields

Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications
Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications

Author: Chowdhury, Niaz

Publisher: IGI Global

Published: 2020-10-30

Total Pages: 255

ISBN-13: 1799858774

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Blockchain technology allows value exchange without the need for a central authority and ensures trust powered by its decentralized architecture. As such, the growing use of the internet of things (IoT) and the rise of artificial intelligence (AI) are to be benefited immensely by this technology that can offer devices and applications data security, decentralization, accountability, and reliable authentication. Bringing together blockchain technology, AI, and IoT can allow these tools to complement the strengths and weaknesses of the others and make systems more efficient. Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications deliberates upon prospects of blockchain technology using AI and IoT devices in various application domains. This book contains a comprehensive collection of chapters on machine learning, IoT, and AI in areas that include security issues of IoT, farming, supply chain management, predictive analytics, and natural languages processing. While highlighting these areas, the book is ideally intended for IT industry professionals, students of computer science and software engineering, computer scientists, practitioners, stakeholders, researchers, and academicians interested in updated and advanced research surrounding the functions of blockchain technology in AI and IoT applications across diverse fields of research.

Federated Learning
Federated Learning

Author: Qiang Yang

Publisher: Springer Nature

Published: 2020-11-25

Total Pages: 291

ISBN-13: 3030630765

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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.”

Split Federated Learning for Secure Iot Applications
Split Federated Learning for Secure Iot Applications

Author: Associate Professor Gururaj Harinahalli Lokesh

Publisher:

Published: 2024-11

Total Pages: 0

ISBN-13: 9781839539459

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This book will review cutting edge technologies and advanced research, which can realize and evaluate the effectiveness and advantages of SplitFed learning for advancing and securing IoTs.

Distributed Artificial Intelligence
Distributed Artificial Intelligence

Author: Satya Prakash Yadav

Publisher:

Published: 2024-10-04

Total Pages: 0

ISBN-13: 9780367638825

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This book provides a deeper understanding of the relevant aspects of AI and DAI impacting each other's efficacy for better output. It will bridge the gap between research solutions and key technologies related to data analytics to ensure Industry 4.0 requirements and at the same time ensure proper network communication and security of big data.

Handbook on Federated Learning
Handbook on Federated Learning

Author: Saravanan Krishnan

Publisher: CRC Press

Published: 2024-01-09

Total Pages: 381

ISBN-13: 1003837522

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Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.