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Federated Learning Over Wireless Edge Networks - Wei Yang Bryan Lim - Bog

Bag om Federated Learning Over Wireless Edge Networks

This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031078408
  • Indbinding:
  • Paperback
  • Sideantal:
  • 184
  • Udgivet:
  • 2. oktober 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 155x11x235 mm.
  • Vægt:
  • 289 g.
  • 8-11 hverdage.
  • 7. december 2024
På lager

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  • BLACK NOVEMBER

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Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Federated Learning Over Wireless Edge Networks

This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively.

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