Federated Edge Learning

Lingua: inglese

Editore: Springer, Springer Aug 2025, 2025

3031966481 / 9783031966484

Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

Venditore con 5 stelle

Venditore AbeBooks dal 23 gennaio 2017

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Descrizione dell’articolo da parte del venditore

This item is printed on demand - Print on Demand Titel. Neuware -This book presents various effective schemes from the perspectives of algorithms, architectures, privacy, and security to enable scalable and trustworthy Federated Edge Learning (FEEL). From the algorithmic perspective, the authors elaborate various federated optimization algorithms, including zeroth-order, first-order, and second-order methods. There is a specific emphasis on presenting provable convergence analysis to illustrate the impact of learning and wireless communication parameters. The convergence rate, computation complexity and communication overhead of the federated zeroth/first/second-order algorithms over wireless networks are elaborated.From the networking architecture perspective, the authors illustrate how the critical challenges of FEEL can be addressed by exploiting different architectures and designing effective communication schemes. Specifically, the communication straggler issue of FEEL can be mitigated by utilizing reconfigurable intelligent surface and unmanned aerial vehicle to reconfigure the propagation environment, while over-the-air computation is utilized to support ultra-fast model aggregation for FEEL by exploiting the waveform superposition property. Additionally, the multi-cell architecture presents a feasible solution for collaborative FEEL training among multiple cells. Finally, the authors discuss the challenges of FEEL from the privacy and security perspective, followed by presenting effective communication schemes that can achieve differentially private model aggregation and Byzantine-resilient model aggregation to achieve trustworthy FEEL.This book is designed for researchers and professionals whose focus is wireless communications. Advanced-level students majoring in computer science and electrical engineering will also find this book useful as a reference.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 208 pp. Englisch.…

Titolo
Federated Edge Learning
Autore
Yong Zhou
Editore
Springer, Springer Aug 2025
Anno di pubblicazione
2025
Condizione
Neu
Rilegatura
Buch
Lingua
inglese
ISBN 10
3031966481
ISBN 13
9783031966484
Peso dell'articolo
481 grammi
Dimensioni
241x160x17 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 23 gennaio 2017

Tariffe di spedizione da Germania a U.S.A.

ArticoloDa 60 a 60 giorni lavorativiDa 60 a 60 giorni lavorativi
Primo articoloEUR 60,00EUR 75,00
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