Federated Learning for Future Intelligent Wireless Networks 1st edition by Yao Sun 1119913917 9781119913917

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ISBN-10 ‏ : ‎1119913917 

ISBN-13 ‏ : ‎9781119913917

Author:    Yao Sun 

Federated Learning for Future Intelligent Wireless Networks
Explore the concepts, algorithms, and applications underlying federated learning

In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy.

Federated Learning for Future Intelligent Wireless Networks 1st Table of contents:

1 Federated Learning with Unreliable Transmission in Mobile Edge Computing Systems
1.1 System Model
1.2 Problem Formulation
1.3 A Joint Optimization Algorithm
1.4 Simulation and Experiment Results
Bibliography
Note
2 Federated Learning with non‐IID data in Mobile Edge Computing Systems
2.1 System Model
2.2 Performance Analysis and Averaging Design
2.3 Data Sharing Scheme
2.4 Simulation Results
Bibliography
Note
3 How Many Resources Are Needed to Support Wireless Edge Networks
3.1 Introduction
3.2 System Model
3.3 Wireless Bandwidth and Computing Resources Consumed for Supporting FL‐Enabled Wireless Edge Networks
3.4 The Relationship between FL Performance and Consumed Resources
3.5 Discussions of Three Cases
3.6 Numerical Results and Discussion
3.7 Conclusion
3.8 Proof of Corollary 3.2
3.9 Proof of Corollary 3.3
References
4 Device Association Based on Federated Deep Reinforcement Learning for Radio Access Network Slicing
4.1 Introduction
4.2 System Model
4.3 Problem Formulation
4.4 Hybrid Federated Deep Reinforcement Learning for Device Association
4.5 Numerical Results
4.6 Conclusion
Acknowledgment
References
5 Deep Federated Learning Based on Knowledge Distillation and Differential Privacy
5.1 Introduction
5.2 Related Work
5.3 System Model
5.4 The Implementation Details of the Proposed Strategy
5.5 Performance Evaluation
5.6 Conclusions
Bibliography
6 Federated Learning‐Based Beam Management in Dense Millimeter Wave Communication Systems
6.1 Introduction
6.2 System Model
6.3 Problem Formulation and Analysis
6.4 FL‐Based Beam Management in UDmmN
6.5 Performance Evaluation
6.6 Conclusions
Bibliography
7 Blockchain‐Empowered Federated Learning Approach for An Intelligent and Reliable D2D Caching Scheme
7.1 Introduction
7.2 Related Work
7.3 System Model
7.4 Problem Formulation and DRL‐Based Model Training
7.5 Privacy‐Preserved and Secure BDRFL Caching Scheme Design
7.6 Consensus Mechanism and Federated Learning Model Update
7.7 Simulation Results and Discussions
7.8 Conclusion
References
8 Heterogeneity‐Aware Dynamic Scheduling for Federated Edge Learning
8.1 Introduction
8.2 Related Works
8.3 System Model for FEEL
8.4 Heterogeneity‐Aware Dynamic Scheduling Problem Formulation
8.5 Dynamic Scheduling Algorithm Design and Analysis
8.6 Evaluation Results
8.7 Conclusions
8.A Appendices
References
Note
9 Robust Federated Learning with Real‐World Noisy Data
9.1 Introduction
9.2 Related Work
9.3 FedCorr
9.4 Experiments
9.5 Further Remarks
Bibliography
10 Analog Over‐the‐Air Federated Learning: Design and Analysis
10.1 Introduction
10.2 System Model
10.3 Analog Over‐the‐Air Model Training
10.4 Convergence Analysis
10.5 Numerical Results
10.6 Conclusion
Bibliography
11 Federated Edge Learning for Massive MIMO CSI Feedback
11.1 Introduction
11.2 System Model
11.3 FEEL for DL‐Based CSI Feedback
11.4 Simulation Results
11.5 Conclusion
Bibliography
Note
12 User‐Centric Decentralized Federated Learning for Autoencoder‐Based CSI Feedback
12.1 Autoencoder‐Based CSI Feedback
12.2 User‐Centric Online Training for AE‐Based CSI Feedback
12.3 Multiuser Online Training Using Decentralized Federated Learning
12.4 Numerical Results
12.5 Conclusion

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Tags: Federated Learning, Future Intelligent, Wireless Networks, Yao Sun