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In den WarenkorbVI, 164 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Springer Proceedings in Materials. Sprache: Englisch.
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Verlag: VDM Verlag Dr. M�ller 2010-11-17, 2010
ISBN 10: 3639307631 ISBN 13: 9783639307634
Sprache: Englisch
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Verlag: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3639307631 ISBN 13: 9783639307634
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Verlag: Taylor & Francis Ltd, London, 2025
ISBN 10: 1032471638 ISBN 13: 9781032471631
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: 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 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Verlag: Taylor & Francis Ltd, London, 2025
ISBN 10: 1032471638 ISBN 13: 9781032471631
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: 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 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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In den WarenkorbZustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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In den WarenkorbZustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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In den Warenkorbhardcover. Zustand: Very Good. Integrative Nanomedicine for New Therapies (Engineering Materials) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .
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Verlag: Taylor & Francis Ltd, London, 2025
ISBN 10: 1032471638 ISBN 13: 9781032471631
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: 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 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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In den WarenkorbPaperback. Zustand: Brand New. 362 pages. 9.18x6.12x9.21 inches. In Stock.
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