Explore the concepts, algorithms, and applications underlying federated learning

InFederated 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.

Readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues apply to wireless communications. Readers will also find:A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FLComprehensive explorations of wireless communication network design and optimization for federated learningPractical discussions of novel federated learning algorithms and frameworks for future wireless networksExpansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution

Perfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning,Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.

Yao Sun, PhD, is a Lecturer with the University of Glasgow in the United Kingdom. He was a former Research Fellow at UESTC in Chengdu, China.

Chaoqun You is a Research Fellow at the Singapore University of Technology and Design. She was formerly an Academic Guest with the Department of Electronic Computer Engineering at the University of Toronto.

Gang Feng is a Professor at the University of Electronic Science and Technology of China. He was an Associate Professor at Nanyang Technological University.

Lei Zhang, PhD, is a Professor at the University of Glasgow, UK. He was formerly a Research Fellow at the 5G Innovation Centre at the University of Surrey.

About the Editors xv

Preface xvii

1 Federated Learning with Unreliable Transmission in Mobile Edge Computing Systems 1Chenyuan 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 23Chenyuan 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 49Yi-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 85Yi-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 113Hui 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 127Qing 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 155Runze 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 181Kun 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 215Jingyi 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 239Howard 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 257Shi 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 273Shi 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