Federated learning iot applications (12 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2022
Serie: Buch 106 von 129 - EAI/Springer Innovations in Communication and Computing
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Sprache: Englisch
Verlag: The Institution of Engineering and Technology, 2024
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Taschenbuch. Zustand: Neu. Federated Learning for IoT Applications | Satya Prakash Yadav (u. a.) | Taschenbuch | EAI/Springer Innovations in Communication and Computing | viii | Englisch | 2023 | Springer | EAN 9783030855611 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[d…ot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Federated Learning for IoT Applications (EAI/Springer Innovations in Communication and Computing)
Yadav, Satya Prakash (Editor) / Bhati, Bhoopesh Singh (Editor) / Mahato, Dharmendra Prasad (Editor) / Kumar, Sachin (Editor)
Sprache: Englisch
Verlag: Springer, 2022
Serie: Buch 106 von 129 - EAI/Springer Innovations in Communication and Computing
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Hardcover. Zustand: Brand New. 273 pages. 9.25x6.10x0.69 inches. In Stock.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 personaliz…ed 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 federatedlearning 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.
Sprache: Englisch
Verlag: Springer, Birkhäuser, 2022
Serie: Buch 106 von 129 - EAI/Springer Innovations in Communication and Computing
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 mode…ls 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 federatedlearning 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.
Sprache: Englisch
Verlag: Institution Of Engineering & Technology Okt 2024, 2024
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Buch. Zustand: Neu. Neuware - New approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller network segments, with each segment trained independen…tly on a server using distributed local client data. The split learning method mitigates two fundamental drawbacks of federated learning: affordability, and privacy and security. When running machine learning computation on devices with limited resources, assigning only a portion of the network to train at the client-side minimizes the processing burden, compared to running a complete network as in federated learning. In addition, neither client nor server has full access to the other, which is more secure. This book reviews cutting edge technologies and advanced research in split federated learning. Coverage includes approaches to realizing and evaluating the effectiveness and advantages of federated learning and split-fed learning, the role of this technology in advancing and securing IoTs, advanced research on emerging AI models for preserving the privacy of the data owned by the clients, and the analysis and development of AI mechanisms in IoT architectures and applications. The use of split federated learning in natural language processing, recommendation systems, healthcare systems, emotion detection, smart agriculture, smart transportation and smart cities is discussed. Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies offers useful insights to the latest developments in the field for researchers, engineers and scientists in academia and industry, who are working in computing, AI, data science and cybersecurity with a focus on federated learning, machine learning and deep learning.
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