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Produktbild: Edge Intelligence in the Making

Edge Intelligence in the Making Optimization, Deep Learning, and Applications

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Beschreibung

This book conducts a comprehensive and detailed survey of the recent research efforts in edge intelligence. The authors first review the background and present motivation for AI running at the network edge. The book then provides an overview of the overarching architectures, frameworks, and emerging...

Beschreibung

This book conducts a comprehensive and detailed survey of the recent research efforts in edge intelligence. The authors first review the background and present motivation for AI running at the network edge. The book then provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. This second edition incorporates the latest research in this rapidly developing area. The authors also highlight the current applications and future research opportunities for edge intelligence.  

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

11.05.2025

Abbildungen

XIV, 299 p. 97 illus., 82 illus. in color.

Verlag Springer

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

11.05.2025

Abbildungen

XIV, 299 p. 97 illus., 82 illus. in color.

Verlag Springer
Seitenzahl

299

Maße (L/B/H)

24.6/17.3/2.2 cm

Gewicht

715 g

Auflage

Second Edition 2025

Sprache

Englisch

ISBN

978-3-031-84562-8

Herstelleradresse

Springer-Verlag GmbH
Europaplatz 3
69115 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Edge Intelligence in the Making
  • Introduction to Edge Intelligence.- Edge Intelligence via Model Training.- Edge Intelligence via Federated Meta-Learning.- Resource-efficient Edge AI via Personalized Federated Learning.- Edge-Cloud Collaborative Learning via Distributionally Robust Optimization.- Hierarchical Mobile-Edge-Cloud Model Training with Hybrid Parallelism.- Communication-Efficient Hierarchical Federated Edge Learning.- Edge Intelligence via Model Inference.- On-Demand Accelerating Deep Neural Network Inference via Edge Computing.- Cooperative Edge DNN Inference with Adaptive Workload Partitioning.- Online Optimization and Resource Provisioning for Edge DNN  Inference.- Applications, Marketplaces, and Future Directions of Edge Intelligence.