Produktbild: Federated Edge Learning

Federated Edge Learning Algorithms, Architectures and Trustworthiness

Aus der Reihe Wireless Networks

Fr. 202.00

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

30.08.2025

Abbildungen

XVI, 39 illus., 35 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Verlag

Springer

Seitenzahl

190

Maße (L/B/H)

24.1/16/1.7 cm

Gewicht

481 g

Sprache

Englisch

ISBN

978-3-031-96648-4

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

30.08.2025

Abbildungen

XVI, 39 illus., 35 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Verlag

Springer

Seitenzahl

190

Maße (L/B/H)

24.1/16/1.7 cm

Gewicht

481 g

Sprache

Englisch

ISBN

978-3-031-96648-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Federated Edge Learning
  • Part 1: Introduction and Overview.- 1. Introduction and overview.- 1.1. Overview of federated edge learning (FEEL).- 1.2. Learning models and algorithms of FEEL.- 1.3. Motivation and challenges of FEEL.- 1.4. Organization.- Part 2: Algorithms.- 2. First-order optimization for FEEL.- 2.1. Background and motivation.- 2.2. Federated first-order optimization model and algorithm.- 2.3. Sparse and low-rank optimization for FEEL.- 2.4. Simulations and discussions.- 2.5. Summary.- 3. Second-order optimization for FEEL.- 3.1. Background and motivation.- 3.2. Federated second-order optimization model and algorithm.- 3.3. Convergence analysis.- 3.4. System optimization.- 3.5. Simulations and discussions.- 3.6. Summary.- 4. Zeroth-order optimization for FEEL.- 4.1. Background and motivation.- 4.2. Federated zeroth-order optimization model and algorithm.- 4.3. Convergence analysis.- 4.4. Over-the-air federated zeroth-order optimization.- 4.5. Simulations and discussions.- 4.6. Summary.- Part 3: Architectures.- 5. Reconfigurable intelligent surface assisted FEEL.- 5.1. Background and motivation.- 5.2. Communication and learning models.- 5.3. Convergence analysis and problem formulation.- 5.4. Alternating optimization algorithm design.- 5.5. GNN-based learning algorithm design.- 5.6. Simulations and discussions.- 5.7. Summary.- 6. Unmanned aerial vehicle assisted FEEL.- 6.1. Background and motivation.- 6.2. Communication and learning models.- 6.3. Convergence analysis and problem formulation.- 6.4. Joint device scheduling, time allocation, and trajectory design.- 6.5. Simulations and discussions.- 6.6. Summary.- 7. FEEL over multi-cellwireless networks.- 7.1. Background and motivation.- 7.2. Communication and learning models.- 7.3. Convergence analysis and problem formulation.- 7.4. Cooperative optimization for multi-cell FEEL.- 7.5. Simulations and discussions.- 7.6. Summary.- Part 4: Trustworthiness.- 8. Differentially-private FEEL.- 8.1. Background and motivation.- 8.2. System model.- 8.3. Performance analysis and privacy preserving mechanism.- 8.4. Two-step alternating low-rank optimization.- 8.5. Simulations and discussions.- 8.6. Summary.- 9. Trustworthy FEEL via blockchain.- 9.1. Background and motivation.- 9.2. System model.- 9.3. Latency analysis and problem formulation.- 9.4. TD3 based resource allocation.- 9.5. Simulations and discussions.- 9.6. Summary.- Part 5: Conclusions and Future Directions.- 10. Conclusions and future directions.- 10.1. Conclusions.- 10.2. Future directions.