دانلود کتاب Federated Learning for Wireless Networks

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Preface Acknowledgement Contents Part I Fundamentals and Background 1 Introduction 1.1 Machine Learning for Wireless Networks 1.1.1 Current Challenges 1.1.2 Distributed Machine Learning 1.1.3 Federated Learning Briefing 1.2 Organization of the Book 2 Fundamentals of Federated Learning 2.1 Introduction and History 2.2 Federated Learning Key Challenges 2.2.1 Statistical Heterogeneity 2.2.2 System Heterogeneity 2.3 Key Design Aspects 2.3.1 Resource Allocation 2.3.2 Incentive Mechanism 2.3.3 Security and Privacy 2.4 Federated Learning Algorithms 2.4.1 FedAvg 2.4.2 FedProx 2.4.3 q-Federated Learning 2.4.4 Federated Multi-Task Learning 2.5 Summary Part II Wireless Federated Learning: Design and Analysis 3 Resource Optimization for Wireless Federated Learning 3.1 Introduction 3.2 Wireless Federated Learning: Convergence Analysis and Resource Allocation 3.2.1 System Model Federated Learning Over Wireless Networks Computation Model Communication Model 3.2.2 Problem Formulation 3.2.3 Decomposition-Based Solution SUB1 Solution SUB2 Solution SUB3 Solution FEDL Solution 3.2.4 Numerical Results Impact of UE Heterogeneity Pareto Optimal Trade-off Impact of η 3.3 Wireless Federated Learning: Resource Allocation and Transmit Power Allocation 3.3.1 Motivation 3.3.2 System Model Machine Learning Model Transmission Model Packet Error Rates Energy Consumption Model Problem Formulation 3.3.3 Convergence Analysis 3.3.4 Optimization of RB Allocation and Transmit Power for FL Training Loss Minimization Optimal Transmit Power Optimal Uplink Resource Block Allocation 3.3.5 Numerical Results 3.4 Collaborative Federated Learning 3.4.1 Motivation 3.4.2 Preliminaries and Overview Original Federated Learning Collaborative Federated Learning 3.4.3 Communication Techniques for Collaborative Federated Learning Network Formation Device Scheduling Coding 3.5 Summary 4 Incentive Mechanisms for Federated Learning 4.1 Introduction 4.2 Game Theory-Enabled Incentive Mechanism 4.2.1 System Model Federated Learning Background Cost Model 4.2.2 Stackelberg Game-Based Solution Incentive Mechanism: A Two-Stage Stackelberg Game Approach Stackelberg Equilibrium: Algorithm and Solution Approach 4.2.3 Simulations 4.3 Auction Theory-Enabled Incentive Mechanism 4.3.1 System Model Preliminary of Federated Learning Computation and Communication Models for Federated Learning Auction Model Deciding Mobile Users's Bid Iterative Algorithm Optimization of Uplink Transmission Power Optimization of CPU Cycle Frequency and Number of Antennas Convergence Analysis Complexity Analysis 4.3.2 Auction Mechanism Between BS and Mobile Users Problem Formulation Approximation Algorithm Design Approximation Ratio Analysis Payment Properties 4.3.3 Simulations 4.4 Summary Appendix A.1 KKT Solution 5 Security and Privacy 5.1 Introduction 5.2 Functional Encryption Enabled Federated Learning 5.2.1 Federated Learning 5.2.2 All or Nothing Transform (AONT) 5.2.3 Multi-Input Functional Encryption for Inner Product 5.2.4 Threat Model 5.3 Secure Aggregation for Wireless Federated Learning 5.3.1 Participant Pre-processing Mode Updates 5.3.2 Secure Aggregation at Aggregator 5.4 Security Analysis 5.4.1 Security for Encryption 5.4.2 Privacy for Participant 5.5 Implementation and Evaluation 5.5.1 Implementation 5.5.2 Evaluation 5.6 Summary 6 Unsupervised Federated Learning 6.1 Introduction 6.2 Problem Formulation 6.3 Dual Averaging Algorithm 6.3.1 Algorithm Description 6.3.2 Data Labeling Step 6.3.3 DA-Based Centroid Computation Step 6.3.4 Weight Computation via Bin Method 6.3.5 Weight Computation via Self-Organizing Maps 6.4 Simulations 6.5 Summary Part III Federated Learning Applications in Wireless Networks 7 Wireless Virtual Reality 7.1 Motivation 7.2 Existing Works 7.3 Representative Work 7.3.1 System Model Transmission Model Break in Presence Model Problem Formulation 7.3.2 Federated Echo State Learning for Predictions of the Users' Location and Orientation Components of Federated ESN Learning Algorithm ESN Based Federated Learning Algorithm for Users' Location and Orientation Predictions 7.3.3 Memory Capacity Analysis 7.3.4 User Association for VR Users 7.3.5 Simulation Results and Analysis 7.4 Summary 8 Vehicular Networks and Autonomous Driving Cars 8.1 Introduction and State of Art 8.2 Vehicular Networks 8.2.1 Selective Model Aggregation 8.2.2 System Model Image Quality Computation Capability Utility Function and Type of Vehicular Client Utility Function of Central Server Global Loss Decay End-to-end Latency 8.2.3 Contract Formulation 8.2.4 Problem Relaxation and Transformation Relaxing Constraint Simplifying Complicated Constraint 8.2.5 Solution to Optimal Contracts 8.2.6 Numerical Results Simulation Settings 8.3 Autonomous Driving Cars 8.3.1 System Model and Problem Formulation Federated Learning Model Communication Model Problem Formulation 8.3.2 Joint Association and Resource Allocation Algorithm for DFL Matching Game-Based Resource Allocation Autonomous Car-RSU Association Algorithm 8.3.3 Numerical Results 8.4 Summary 9 Smart Industries and Intelligent Reflecting Surfaces 9.1 Smart Industry 9.1.1 System Model and Problem Formulation 9.1.2 Block Successive Upper-Bound Minimization-Based Solution 9.1.3 Simulations 9.2 Intelligent Reflecting Surfaces 9.2.1 Introduction 9.2.2 Problem Formulation 9.2.3 FL Assisted Optimal Beam Reflection 9.2.4 Simulation 9.3 Summary References