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| |  Online-Ressource |
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| Titel: | Federated learning systems |
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| Titelzusatz: | towards next-generation AI |
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| Mitwirkende: | Rehman, Muhammad Habib ur [HerausgeberIn]  |
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| | Gaber, Mohamed Medhat [HerausgeberIn] |
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| Verf.angabe: | Muhammad Habib ur Rehman, Mohamed Medhat Gaber (editors) |
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| Verlagsort: | Cham |
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| Verlag: | Springer |
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| E-Jahr: | 2021 |
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| Jahr: | [2021] |
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| Umfang: | 1 Online-Ressource (xvi, 196 Seiten) |
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| Illustrationen: | Illustrationen, Diagramme |
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| Gesamttitel/Reihe: | Studies in computational intelligence ; volume 965 |
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| Fussnoten: | Description based on publisher supplied metadata and other sources |
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| ISBN: | 978-3-030-70604-3 |
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| Abstract: | Intro -- Preface -- Contents -- Contributors -- Acronyms -- 1 Federated Learning Research: Trends and Bibliometric Analysis -- 1.1 Introduction -- 1.2 Material and Method -- 1.2.1 Data Collection -- 1.2.2 Data Analysis -- 1.3 Results and Discussion -- 1.3.1 Growth Pattern Over the Years -- 1.3.2 Top Cited Papers -- 1.3.3 Productivity Measures -- 1.3.4 Domain Profile -- 1.4 Related Work -- 1.5 Conclusion and Future Research Directions -- References -- 2 A Review of Privacy-Preserving Federated Learning for the Internet-of-Things -- 2.1 Introduction -- 2.2 Distributed Machine Learning -- 2.2.1 Concurrency -- 2.2.2 Model Consistency -- 2.2.3 Centralized Versus Decentralized Learning -- 2.3 Federated Learning -- 2.3.1 Overview -- 2.3.2 Specific Challenges for FL in IoT Context -- 2.3.3 Applied FL Research -- 2.4 Privacy Preservation -- 2.4.1 Privacy Preserving Methods -- 2.5 Privacy Preservation in FL -- 2.6 Challenges in Applying Privacy-Preserving FL to the IoT -- 2.6.1 Optimal Model Architecture/Hyperparameters -- 2.6.2 Continual Learning -- 2.6.3 Better Privacy-Preserving Methods -- 2.6.4 FL Combined with Fog Computing -- 2.6.5 FL on Low Power Devices -- 2.7 Conclusion -- References -- 3 Differentially Private Federated Learning: Algorithm, Analysis and Optimization -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Federated Learning -- 3.2.2 Differential Privacy -- 3.2.3 Threat Model -- 3.3 Federated Learning with Differential Privacy -- 3.3.1 Global Differential Privacy -- 3.3.2 Proposed NbAFL -- 3.4 Convergence Analysis on NbAFL -- 3.5 K-Client Random Scheduling Policy -- 3.6 Differentially Private FL Based Client Selection -- 3.6.1 Algorithm Description -- 3.6.2 Noise Recalculation for Varying K -- 3.7 Experimental Results -- 3.7.1 Performance Evaluation on Protection Levels -- 3.7.2 Impact of the Number of Chosen Clients K. |
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| URL: | Aggregator: https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=6640494 |
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| Schlagwörter: | (s)Deep learning / (s)Maschinelles Lernen |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Bibliogr. Hinweis: | Erscheint auch als : Druck-Ausgabe: Federated Learning Systems. - Cham : Springer, 2021. - xvi, 196 Seiten |
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| Sach-SW: | Electronic books |
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| K10plus-PPN: | 1793808376 |
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| Verknüpfungen: | → Übergeordnete Aufnahme |
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| Lokale URL UB: |  Zum Volltext |
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978-3-030-70604-3
Federated learning systems / Rehman, Muhammad Habib ur [HerausgeberIn]; [2021] (Online-Ressource)
68882096
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