Energy optimization security federated (12 risultati)
Energy Optimization and Security in Federated Learning for Iot Environments
Balusamy, Balamurugan (EDT); Arockiam, Daniel (EDT); Raj, Pethuru (EDT)
Lingua: Inglese
Editore: The Institution of Engineering and Technology, 2025
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 128,76
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Condizione: 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)
Lingua: Inglese
Editore: The Institution of Engineering and Technology, 2025
- Rilegato
Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 134,56
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Condizione: New.
Energy Optimization and Security in Federated Learning for IoT Environments (Computing and Networks)
Lingua: Inglese
Editore: The Institution of Engineering and Technology, 2025
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Da: California Books, Miami, FL, U.S.A.California Books
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EUR 136,92
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Condizione: New.
Energy Optimization and Security in Federated Learning for Iot Environments
Balusamy, Balamurugan (EDT); Arockiam, Daniel (EDT); Raj, Pethuru (EDT)
Lingua: Inglese
Editore: The Institution of Engineering and Technology, 2025
- Rilegato
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 135,34
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Condizione: As New. Unread book in perfect condition.
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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EUR 161,62
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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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EUR 155,55
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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Energy Optimization and Security in Federated Learning for IoT Environments (Computing and Networks)
Lingua: Inglese
Editore: The Institution of Engineering and Technology, 2025
- Rilegato
Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 152,97
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Condizione: New. In.
Energy Optimization and Security in Federated Learning for Iot Environments
Balusamy, Balamurugan (EDT); Arockiam, Daniel (EDT); Raj, Pethuru (EDT)
Lingua: Inglese
Editore: The Institution of Engineering and Technology, 2025
- Rilegato
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 152,96
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Condizione: New.
Energy Optimization and Security in Federated Learning for Iot Environments
Balusamy, Balamurugan (Editor)/ Arockiam, Daniel (Editor)/ Raj, Pethuru (Editor)
- Rilegato
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 169,17
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Hardcover. Condizione: Brand New. 350 pages. 9.21x6.14x9.21 inches. In Stock.
Lingua: Inglese
Editore: Institution of Engineering and Technology, GB, 2025
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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EUR 195,73
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Hardback. Condizione: 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 t…he 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.
Lingua: Inglese
Editore: Institution Of Engineering & Technology Feb 2025, 2025
- Rilegato
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 176,00
EUR 63,36 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Buch. Condizione: 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. Du…e 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.
Lingua: Inglese
Editore: Institution of Engineering and Technology, GB, 2025
- Rilegato
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 190,94
EUR 75,65 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Hardback. Condizione: 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 t…he 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.
