Federated learning for future intelligent wireless networks / edited by Yao Sun... [and 3 others].

Format:

Book

Contributor:

Sun, Yao, editor.

Wiley InterScience (Online service)

Language:

English

Subjects (All):

Machine learning--Industrial applications.

Machine learning.

Physical Description:

1 online resource (xx, 294 pages) : illustrations (chiefly color)

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc., [2024]

Contents:

About the Editors xv

Preface xvii

1 Federated Learning with Unreliable Transmission in Mobile Edge Computing Systems 1 Chenyuan Feng, Daquan Feng, Zhongyuan Zhao, Howard H. Yang, and Tony Q. S. Quek

1.1 System Model 1

1.2 Problem Formulation 4

1.3 A Joint Optimization Algorithm 10

1.4 Simulation and Experiment Results 16

2 Federated Learning with non-IID data in Mobile Edge Computing Systems 23 Chenyuan Feng, Daquan Feng, Zhongyuan Zhao, Geyong Min, and Hancong Duan

2.1 System Model 23

2.2 Performance Analysis and Averaging Design 24

2.3 Data Sharing Scheme 30

2.4 Simulation Results 42

3 How Many Resources Are Needed to Support Wireless Edge Networks 49 Yi-Jing Liu, Gang Feng, Yao Sun, and Shuang Qin

3.1 Introduction 49

3.2 System Model 50

3.3 Wireless Bandwidth and Computing Resources Consumed for Supporting FL-EnabledWireless Edge Networks 54

3.4 The Relationship between FL Performance and Consumed Resources 59

3.5 Discussions of Three Cases 62

3.6 Numerical Results and Discussion 67

3.7 Conclusion 75

3.8 Proof of Corollary 3.2 76

3.9 Proof of Corollary 3.3 77

4 Device Association Based on Federated Deep Reinforcement Learning for Radio Access Network Slicing 85 Yi-Jing Liu, Gang Feng, Yao Sun, and Shuang Qin

4.1 Introduction 85

4.2 System Model 87

4.3 Problem Formulation 90

4.4 Hybrid Federated Deep Reinforcement Learning for Device Association 94

4.5 Numerical Results 103

4.6 Conclusion 109

5 Deep Federated Learning Based on Knowledge Distillation and Differential Privacy 113 Hui Lin, Feng Yu, and Xiaoding Wang

5.1 Introduction 113

5.2 RelatedWork 115

5.3 System Model 118

5.4 The Implementation Details of the Proposed Strategy 119

5.5 Performance Evaluation 120

5.6 Conclusions 122

6 Federated Learning-Based Beam Management in Dense Millimeter Wave Communication Systems 127 Qing Xue and Liu Yang

6.1 Introduction 127

6.2 System Model 130

6.3 Problem Formulation and Analysis 133

6.4 FL-Based Beam Management in UDmmN 135

6.6 Conclusions 150

7 Blockchain-Empowered Federated Learning Approach for An Intelligent and Reliable D2D Caching Scheme 155 Runze Cheng, Yao Sun, Yijing Liu, Le Xia, Daquan Feng, and Muhammad Imran

7.1 Introduction 155

7.2 RelatedWork 157

7.3 System Model 159

7.4 Problem Formulation and DRL-Based Model Training 160

7.5 Privacy-Preserved and Secure BDRFL Caching Scheme Design 165

7.6 Consensus Mechanism and Federated Learning Model Update 170

7.7 Simulation Results and Discussions 173

7.8 Conclusion 177

8 Heterogeneity-Aware Dynamic Scheduling for Federated Edge Learning 181 Kun Guo, Zihan Chen, Howard H. Yang, and Tony Q. S. Quek

8.1 Introduction 181

8.2 RelatedWorks 184

8.3 System Model for FEEL 185

8.4 Heterogeneity-Aware Dynamic Scheduling Problem Formulation 189

8.5 Dynamic Scheduling Algorithm Design and Analysis 192

8.6 Evaluation Results 197

8.7 Conclusions 208

8.A.1 Proof of Theorem 8.2 208

8.A.2 Proof of Theorem 8.3 209

9 Robust Federated Learning with Real-World Noisy Data 215 Jingyi Xu, Zihan Chen, Tony Q. S. Quek, and Kai Fong Ernest Chong

9.1 Introduction 215

9.2 RelatedWork 217

9.3 FedCorr 219

9.4 Experiments 226

9.5 Further Remarks 232

10 Analog Over-the-Air Federated Learning: Design and Analysis 239 Howard H. Yang, Zihan Chen, and Tony Q. S. Quek

10.1 Introduction 239

10.2 System Model 241

10.3 Analog Over-the-Air Model Training 242

10.4 Convergence Analysis 245

10.5 Numerical Results 250

10.6 Conclusion 253

11 Federated Edge Learning for Massive MIMO CSI Feedback 257 Shi Jin, Yiming Cui, and Jiajia Guo

11.1 Introduction 257

11.2 System Model 259

11.3 FEEL for DL-Based CSI Feedback 260

11.4 Simulation Results 264

11.5 Conclusion 268

12 User-Centric Decentralized Federated Learning for Autoencoder-Based CSI Feedback 273 Shi Jin, Jiajia Guo, Yan Lv, and Yiming Cui

12.1 Autoencoder-Based CSI Feedback 273

12.2 User-Centric Online Training for AE-Based CSI Feedback 275

12.3 Multiuser Online Training Using Decentralized Federated Learning 279

12.4 Numerical Results 283

12.5 Conclusion 287

Bibliography 287

Index 291.

Notes:

Includes bibliographical references and index.

Electronic reproduction. Hoboken, N.J. Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on December 06, 2023).

Other Format:

Print version:

ISBN:

9781119913924

1119913926

9781119913900

111991390X

9781119913917

1119913918

Publisher Number:

40032069392

Access Restriction:

Restricted for use by site license.