9781839539626 - energy optimization and security in federated learning for iot environments (computing and networks) de balusamy, balamurugan (13 resultados)
Energy Optimization and Security in Federated Learning for Iot Environments
Balusamy, Balamurugan (EDT); Arockiam, Daniel (EDT); Raj, Pethuru (EDT)
Idioma: Inglés
Editorial: The Institution of Engineering and Technology, 2025
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 126,34
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Energy Optimization and Security in Federated Learning for IoT Environments (Computing and Networks)
Idioma: Inglés
Editorial: The Institution of Engineering and Technology, 2025
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 128,71
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Energy Optimization and Security in Federated Learning for Iot Environments
Balusamy, Balamurugan (EDT); Arockiam, Daniel (EDT); Raj, Pethuru (EDT)
Idioma: Inglés
Editorial: The Institution of Engineering and Technology, 2025
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 129,45
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Condición: As New. Unread book in perfect condition.
Energy Optimization and Security in Federated Learning for Iot Environments
Balusamy, Balamurugan (EDT); Arockiam, Daniel (EDT); Raj, Pethuru (EDT)
Idioma: Inglés
Editorial: The Institution of Engineering and Technology, 2025
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 137,67
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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EUR 156,13
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HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
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EUR 161,30
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HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Energy Optimization and Security in Federated Learning for IoT Environments (Computing and Networks)
Idioma: Inglés
Editorial: The Institution of Engineering and Technology, 2025
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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EUR 153,54
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Condición: New. In.
Energy Optimization and Security in Federated Learning for Iot Environments
Balusamy, Balamurugan (EDT); Arockiam, Daniel (EDT); Raj, Pethuru (EDT)
Idioma: Inglés
Editorial: The Institution of Engineering and Technology, 2025
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 148,80
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Condición: New.
Energy Optimization and Security in Federated Learning for Iot Environments
Balusamy, Balamurugan (Editor)/ Arockiam, Daniel (Editor)/ Raj, Pethuru (Editor)
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 173,01
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Hardcover. Condición: Brand New. 350 pages. 9.21x6.14x9.21 inches. In Stock.
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Idioma: Inglés
Editorial: Institution of Engineering and Technology, GB, 2025
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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EUR 194,09
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Hardback. Condición: New. Smart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particularly in settings with limited energy resources. Due to th…e significant energy consumption and communication overhead associated with delivering huge amounts of data, traditional machine learning algorithms which rely on centralized cloud servers for training are not always suitable. Federated learning is a decentralized strategy that enables collaborative machine learning model training while keeping the data local on edge devices. It has emerged as a suitable solution to overcome the energy constraints of IoT devices. Federated learning works by dividing the training process among several nodes and using the processing power of edge devices. As opposed to sending raw data to a central server, only the model changes are communicated thereby considerably lowering the communication costs while protecting data privacy. This strategy reduces energy usage while simultaneously reducing network latency and bandwidth-related problems. In this book, the authors show how to optimise federated learning algorithms and develop new communication protocols and resource allocation methodologies to maximize energy savings while retaining respectable model accuracy, to develop long-lasting and scalable IoT solutions that can function independently with no dependency on an external cloud infrastructure. Energy Optimization and Security in Federated Learning for IoT Environments is intended to be a useful resource for academic researchers, RandD professionals, IoT engineers in the IT industry, and data scientists creating optimised AI models to be run in cloud environments.
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Idioma: Inglés
Editorial: Institution Of Engineering & Technology Feb 2025, 2025
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 177,10
Envío por EUR 63,36Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Buch. Condición: Neu. Neuware - Smart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particularly in settings with limited energy resources. Due… to the significant energy consumption and communication overhead associated with delivering huge amounts of data, traditional machine learning algorithms which rely on centralized cloud servers for training are not always suitable. Federated learning is a decentralized strategy that enables collaborative machine learning model training while keeping the data local on edge devices. It has emerged as a suitable solution to overcome the energy constraints of IoT devices. Federated learning works by dividing the training process among several nodes and using the processing power of edge devices. As opposed to sending raw data to a central server, only the model changes are communicated thereby considerably lowering the communication costs while protecting data privacy. This strategy reduces energy usage while simultaneously reducing network latency and bandwidth-related problems. In this book, the authors show how to optimise federated learning algorithms and develop new communication protocols and resource allocation methodologies to maximize energy savings while retaining respectable model accuracy, to develop long-lasting and scalable IoT solutions that can function independently with no dependency on an external cloud infrastructure. Energy Optimization and Security in Federated Learning for IoT Environments is intended to be a useful resource for academic researchers, R&D professionals, IoT engineers in the IT industry, and data scientists creating optimised AI models to be run in cloud environments.
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Idioma: Inglés
Editorial: Institution of Engineering and Technology, GB, 2025
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 184,80
Envío por EUR 75,93Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Hardback. Condición: New. Smart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particularly in settings with limited energy resources. Due to th…e significant energy consumption and communication overhead associated with delivering huge amounts of data, traditional machine learning algorithms which rely on centralized cloud servers for training are not always suitable. Federated learning is a decentralized strategy that enables collaborative machine learning model training while keeping the data local on edge devices. It has emerged as a suitable solution to overcome the energy constraints of IoT devices. Federated learning works by dividing the training process among several nodes and using the processing power of edge devices. As opposed to sending raw data to a central server, only the model changes are communicated thereby considerably lowering the communication costs while protecting data privacy. This strategy reduces energy usage while simultaneously reducing network latency and bandwidth-related problems. In this book, the authors show how to optimise federated learning algorithms and develop new communication protocols and resource allocation methodologies to maximize energy savings while retaining respectable model accuracy, to develop long-lasting and scalable IoT solutions that can function independently with no dependency on an external cloud infrastructure. Energy Optimization and Security in Federated Learning for IoT Environments is intended to be a useful resource for academic researchers, RandD professionals, IoT engineers in the IT industry, and data scientists creating optimised AI models to be run in cloud environments.
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Librería: THE SAINT BOOKSTORE, Southport, Reino UnidoTHE SAINT BOOKSTORE
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EUR 159,12
Envío por EUR 18,70Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
