9781032471631 - handbook on federated learning: advances, applications and opportunities (13 risultati)
Handbook on Federated Learning : Advances, Applications and Opportunities
Krishnan, Saravanan (EDT); Anand, A. Jose (EDT); Srinivasan, R. (EDT); Kavitha, R. (EDT); Suresh, S. (EDT)
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 60,84
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Condizione: New.
Handbook on Federated Learning : Advances, Applications and Opportunities
Krishnan, Saravanan (EDT); Anand, A. Jose (EDT); Srinivasan, R. (EDT); Kavitha, R. (EDT); Suresh, S. (EDT)
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 72,14
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Condizione: As New. Unread book in perfect condition.
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Da: California Books, Miami, FL, U.S.A.California Books
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EUR 77,44
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Condizione: New.
Handbook on Federated Learning : Advances, Applications and Opportunities
Krishnan, Saravanan (EDT); Anand, A. Jose (EDT); Srinivasan, R. (EDT); Kavitha, R. (EDT); Suresh, S. (EDT)
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 64,98
EUR 17,64 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 10 disponibili
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 77,10
EUR 7,64 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 3 disponibili
Condizione: New.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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EUR 80,75
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Handbook on Federated Learning : Advances, Applications and Opportunities
Krishnan, Saravanan (EDT); Anand, A. Jose (EDT); Srinivasan, R. (EDT); Kavitha, R. (EDT); Suresh, S. (EDT)
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 73,11
EUR 17,64 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 10 disponibili
Condizione: As New. Unread book in perfect condition.
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 90,67
EUR 3,49 spedizioneSpedito in U.S.A.Quantità: 4 disponibili
Condizione: New. 1st edition NO-PA16APR2015-KAP.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 90,24
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 3 disponibili
Condizione: New.
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 102,33
EUR 14,70 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 2 disponibili
Paperback. Condizione: Brand New. 362 pages. 9.18x6.12x9.21 inches. In Stock.
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- Print on Demand
Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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EUR 63,23
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days.
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- Print on Demand
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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EUR 64,66
Spedizione gratuitaSpedito in U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. 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 imp…ortant 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. Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Brossura
- Print on Demand
Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 69,03
EUR 43,51 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. 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 imp…ortant 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. Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
