{"product_id":"federated-learning-for-future-intelligent-wireless-networks-9781119913894","title":"Federated Learning for Future Intelligent","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eFederated Learning for Future Intelligent Wireless Networks\u003c\/b\u003e \u003cp\u003e\u003cb\u003eExplore the concepts, algorithms, and applications underlying federated learning\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eFederated Learning for Future Intelligent Wireless Networks\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003eReaders 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: \u003c\/p\u003e\u003cul\u003e\u003cli\u003eA thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and h\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAbout the Editors xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Federated Learning with Unreliable Transmission in Mobile Edge Computing Systems 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChenyuan Feng, Daquan Feng, Zhongyuan Zhao, Howard H. Yang, and Tony Q. S. Quek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 System Model 1\u003c\/p\u003e \u003cp\u003e1.2 Problem Formulation 4\u003c\/p\u003e \u003cp\u003e1.3 A Joint Optimization Algorithm 10\u003c\/p\u003e \u003cp\u003e1.4 Simulation and Experiment Results 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Federated Learning with non-IID data in Mobile Edge Computing Systems 23\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChenyuan Feng, Daquan Feng, Zhongyuan Zhao, Geyong Min, and Hancong Duan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 System Model 23\u003c\/p\u003e \u003cp\u003e2.2 Performance Analysis and Averaging Design 24\u003c\/p\u003e \u003cp\u003e2.3 Data Sharing Scheme 30\u003c\/p\u003e \u003cp\u003e2.4 Simulation Results 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 How Many Resources Are Needed to Support Wireless Edge Networks 49\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYi-Jing Liu, Gang Feng, Yao Sun, and Shuang Qin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 49\u003c\/p\u003e \u003cp\u003e3.2 System Model 50\u003c\/p\u003e \u003cp\u003e3.3 Wireless Bandwidth and Computing Resources Consumed for Supporting FL-EnabledWireless Edge Networks 54\u003c\/p\u003e \u003cp\u003e3.4 The Relationship between FL Performance and Consumed Resources 59\u003c\/p\u003e \u003cp\u003e3.5 Discussions of Three Cases 62\u003c\/p\u003e \u003cp\u003e3.6 Numerical Results and Discussion 67\u003c\/p\u003e \u003cp\u003e3.7 Conclusion 75\u003c\/p\u003e \u003cp\u003e3.8 Proof of Corollary 3.2 76\u003c\/p\u003e \u003cp\u003e3.9 Proof of Corollary 3.3 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Device Association Based on Federated Deep Reinforcement Learning for Radio Access Network Slicing 85\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYi-Jing Liu, Gang Feng, Yao Sun, and Shuang Qin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 85\u003c\/p\u003e \u003cp\u003e4.2 System Model 87\u003c\/p\u003e \u003cp\u003e4.3 Problem Formulation 90\u003c\/p\u003e \u003cp\u003e4.4 Hybrid Federated Deep Reinforcement Learning for Device Association 94\u003c\/p\u003e \u003cp\u003e4.5 Numerical Results 103\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Deep Federated Learning Based on Knowledge Distillation and Differential Privacy 113\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHui Lin, Feng Yu, and Xiaoding Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 113\u003c\/p\u003e \u003cp\u003e5.2 RelatedWork 115\u003c\/p\u003e \u003cp\u003e5.3 System Model 118\u003c\/p\u003e \u003cp\u003e5.4 The Implementation Details of the Proposed Strategy 119\u003c\/p\u003e \u003cp\u003e5.5 Performance Evaluation 120\u003c\/p\u003e \u003cp\u003e5.6 Conclusions 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Federated Learning-Based Beam Management in Dense Millimeter Wave Communication Systems 127\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eQing Xue and Liu Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 127\u003c\/p\u003e \u003cp\u003e6.2 System Model 130\u003c\/p\u003e \u003cp\u003e6.3 Problem Formulation and Analysis 133\u003c\/p\u003e \u003cp\u003e6.4 FL-Based Beam Management in UDmmN 135\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Blockchain-Empowered Federated Learning Approach for An Intelligent and Reliable D2D Caching Scheme 155\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRunze Cheng, Yao Sun, Yijing Liu, Le Xia, Daquan Feng, and Muhammad Imran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 155\u003c\/p\u003e \u003cp\u003e7.2 RelatedWork 157\u003c\/p\u003e \u003cp\u003e7.3 System Model 159\u003c\/p\u003e \u003cp\u003e7.4 Problem Formulation and DRL-Based Model Training 160\u003c\/p\u003e \u003cp\u003e7.5 Privacy-Preserved and Secure BDRFL Caching Scheme Design 165\u003c\/p\u003e \u003cp\u003e7.6 Consensus Mechanism and Federated Learning Model Update 170\u003c\/p\u003e \u003cp\u003e7.7 Simulation Results and Discussions 173\u003c\/p\u003e \u003cp\u003e7.8 Conclusion 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Heterogeneity-Aware Dynamic Scheduling for Federated Edge Learning 181\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKun Guo, Zihan Chen, Howard H. Yang, and Tony Q. S. Quek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 181\u003c\/p\u003e \u003cp\u003e8.2 RelatedWorks 184\u003c\/p\u003e \u003cp\u003e8.3 System Model for FEEL 185\u003c\/p\u003e \u003cp\u003e8.4 Heterogeneity-Aware Dynamic Scheduling Problem Formulation 189\u003c\/p\u003e \u003cp\u003e8.5 Dynamic Scheduling Algorithm Design and Analysis 192\u003c\/p\u003e \u003cp\u003e8.6 Evaluation Results 197\u003c\/p\u003e \u003cp\u003e8.7 Conclusions 208\u003c\/p\u003e \u003cp\u003e8.A.1 Proof of Theorem 8.2 208\u003c\/p\u003e \u003cp\u003e8.A.2 Proof of Theorem 8.3 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Robust Federated Learning with Real-World Noisy Data 215\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJingyi Xu, Zihan Chen, Tony Q. S. Quek, and Kai Fong Ernest Chong\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 215\u003c\/p\u003e \u003cp\u003e9.2 RelatedWork 217\u003c\/p\u003e \u003cp\u003e9.3 FedCorr 219\u003c\/p\u003e \u003cp\u003e9.4 Experiments 226\u003c\/p\u003e \u003cp\u003e9.5 Further Remarks 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Analog Over-the-Air Federated Learning: Design and Analysis 239\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHoward H. Yang, Zihan Chen, and Tony Q. S. Quek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 239\u003c\/p\u003e \u003cp\u003e10.2 System Model 241\u003c\/p\u003e \u003cp\u003e10.3 Analog Over-the-Air Model Training 242\u003c\/p\u003e \u003cp\u003e10.4 Convergence Analysis 245\u003c\/p\u003e \u003cp\u003e10.5 Numerical Results 250\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Federated Edge Learning for Massive MIMO CSI Feedback 257\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShi Jin, Yiming Cui, and Jiajia Guo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 257\u003c\/p\u003e \u003cp\u003e11.2 System Model 259\u003c\/p\u003e \u003cp\u003e11.3 FEEL for DL-Based CSI Feedback 260\u003c\/p\u003e \u003cp\u003e11.4 Simulation Results 264\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 User-Centric Decentralized Federated Learning for Autoencoder-Based CSI Feedback 273\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShi Jin, Jiajia Guo, Yan Lv, and Yiming Cui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Autoencoder-Based CSI Feedback 273\u003c\/p\u003e \u003cp\u003e12.2 User-Centric Online Training for AE-Based CSI Feedback 275\u003c\/p\u003e \u003cp\u003e12.3 Multiuser Online Training Using Decentralized Federated Learning 279\u003c\/p\u003e \u003cp\u003e12.4 Numerical Results 283\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 287\u003c\/p\u003e \u003cp\u003eBibliography 287\u003c\/p\u003e \u003cp\u003eIndex 291\u003c\/p\u003e\n\u003c\/li\u003e\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":51039273386327,"sku":"9781119913894","price":99.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119913894.jpg?v=1750943128","url":"https:\/\/bookcurl.com\/products\/federated-learning-for-future-intelligent-wireless-networks-9781119913894","provider":"Book Curl","version":"1.0","type":"link"}