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- PublisherTredition Gmbh
- Publication date2024
- ISBN 10 3384254228
- ISBN 13 9783384254221
- BindingPaperback
- LanguageEnglish
- Number of pages86
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Distributed Learning with a Local Touch (Paperback)
Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
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Paperback. Condition: new. Paperback. Multiparty learning as an emerging topic, many of the related frameworks and ap-plications are proposed. In this section, we explore the extent of these frameworks and technologies. Yang et al.72 provide a comprehensive survey of existing works on a secure fed-erated learning framework. Bonawitz et al.8 build a scalable production system for Federated Learning in the domain of mobile devices. Konecn`yetal.30 propose ways to reduce communication costs in federated learning. Nishio and Yonetani44 propose a new Federated Learning protocol, FedCS, which can actively manage computing workers based on their resource conditions. Zhao et al.75 notice that conventional federated learning fails on learning non-IID data and propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Smith et al.63 propose fed-erated multi-task learning, which is a novel systems-aware optimization method, MOCHA. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783384254221
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Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning
Print on DemandSeller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 84 pp. Englisch. Seller Inventory # 9783384254221
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Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Multiparty learning as an emerging topic, many of the related frameworks and ap-plications are proposed. In this section, we explore the extent of these frameworks and technologies.Yang et al.72 provide a comprehensive survey of existing works on a secure fed-erated learning framework. Bonawitz et al.8 build a scalable production system for Federated Learning in the domain of mobile devices. Konecn`yetal.30 propose ways to reduce communication costs in federated learning. Nishio and Yonetani44 propose a new Federated Learning protocol, FedCS, which can actively manage computing workers based on their resource conditions. Zhao et al.75 notice that conventional federated learning fails on learning non-IID data and propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Smith et al.63 propose fed-erated multi-task learning, which is a novel systems-aware optimization method, MOCHA. Seller Inventory # 9783384254221
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Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning
Print on DemandSeller: moluna, Greven, Germany
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Multiparty learning as an emerging topic, many of the related frameworks and ap-plications are proposed. In this section, we explore the extent of these frameworks and technologies.Yang et al.72 provide a comprehensive survey of existing works on a secure f. Seller Inventory # 1704205624
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Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning
Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. Distributed Learning with a Local Touch: Improving Efficiency in Multiparty Learning | Shiva | Taschenbuch | Paperback | Englisch | 2024 | tredition | EAN 9783384254221 | Verantwortliche Person für die EU: tredition GmbH, Heinz-Beusen-Stieg 5, 22926 Ahrensburg, support[at]tredition[dot]com | Anbieter: preigu. Seller Inventory # 129389367
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