Federated Learning for Future Intelligent Wireless Networks

• Autore: Sun Yao
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2023; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (317 pages)
• Altri autori: YouChaoqun FengGang ZhangLei
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- About the Editors -- Preface -- Chapter 1 Federated Learning with Unreliable Transmission in Mobile Edge Computing Systems -- 1.1 System Model -- 1.1.1 Local Model Training -- 1.1.2 Update Result Feedback via the Wireless Channels -- 1.1.3 Global Model Averaging -- 1.2 Problem Formulation -- 1.2.1 Model Accuracy Loss -- 1.2.2 Communication Loss ϵt,mC -- 1.2.3 Sample Selection Loss ϵt,mS -- 1.2.4 Model Training Loss ϵt,mM -- 1.2.5 Problem Formulation -- 1.2.5.1 Objective Function -- 1.2.5.2 Energy Consumption Constraint -- 1.2.5.3 User Selection Constraint -- 1.2.5.4 Data Volume Constraint of Local Training Datasets -- 1.3 A Joint Optimization Algorithm -- 1.3.1 Compression Optimization -- 1.3.1.1 Optimization of At,m -- 1.3.1.2 Optimization of Dt,m -- 1.3.2 Joint Optimization of At,m and Dt,m -- 1.3.3 Optimization of Sample Selection -- 1.3.4 Optimization of User Selection -- 1.3.5 A Joint Optimization Algorithm -- 1.4 Simulation and Experiment Results -- Bibliography -- Chapter 2 Federated Learning with non‐IID data in Mobile Edge Computing Systems -- 2.1 System Model -- 2.1.1 Local Model Training -- 2.1.2 Federated Averaging -- 2.2 Performance Analysis and Averaging Design -- 2.2.1 The Analysis of Expected Weight Divergence -- 2.2.1.1 The Analysis of Expected Data Distribution Divergence E{ℒm} -- 2.2.1.2 An Upper Bound of δ(tK) -- 2.2.2 Rethinking the Settings of Federated Averaging Weights -- 2.3 Data Sharing Scheme -- 2.3.1 Data Sharing -- 2.3.2 Problem Formation -- 2.3.2.1 Objective Function -- 2.3.3 Optimization Constraints -- 2.3.4 A Joint Optimization Algorithm -- 2.3.4.1 CPU Cycle Frequency Optimization Subproblem -- 2.3.4.2 Transmit Power Allocation Subproblem -- 2.3.4.3 Sharing Dataset Optimization Subproblem -- 2.3.4.4 User Selection Optimization Subproblem. ; 2.3.4.5 A Joint Optimization Algorithm -- 2.4 Simulation Results -- Bibliography -- Chapter 3 How Many Resources Are Needed to Support Wireless Edge Networks -- 3.1 Introduction -- 3.2 System Model -- 3.2.1 FL Model -- 3.2.1.1 Loss Function -- 3.2.1.2 Updating Model -- 3.2.2 Computing Resource Consumption Model -- 3.2.3 Communication Resource Consumption Model -- 3.2.3.1 Uplink -- 3.2.3.2 DownLink -- 3.3 Wireless Bandwidth and Computing Resources Consumed for Supporting FL‐Enabled Wireless Edge Networks -- 3.3.1 SINR Analysis (Uplink Direction) -- 3.3.1.1 Probability Density Function (PDF) of SINR -- 3.3.1.2 Transmission Success Probability of Local Models -- 3.3.2 SNR Analysis (Downlink Direction) -- 3.3.3 Wireless Bandwidth Needed for Transmitting Local/Global Models -- 3.3.4 Computing Resources Needed for Training Local Models -- 3.4 The Relationship between FL Performance and Consumed Resources -- 3.4.1 Local Model Accuracy -- 3.4.2 Global Model Accuracy -- 3.5 Discussions of Three Cases -- 3.5.1 Case 1: Sufficient Communication Resources and Computing Resources -- 3.5.2 Case 2: Sufficient Computing Resources and Insufficient Communication Resources -- 3.5.3 Case 3: Sufficient Communication Resources and Insufficient Computing Resources -- 3.6 Numerical Results and Discussion -- 3.6.1 Simulation Setting -- 3.6.2 Simulation Results -- 3.6.2.1 Verifying Analytical Results -- 3.6.2.2 Measuring the Performance of FL Settings -- 3.6.2.3 Examining the Trade‐Off between the Computing and Communication Resources under FL Framework -- 3.7 Conclusion -- 3.8 Proof of Corollary 3.2 -- 3.9 Proof of Corollary 3.3 -- References -- Chapter 4 Device Association Based on Federated Deep Reinforcement Learning for Radio Access Network Slicing -- 4.1 Introduction -- 4.2 System Model -- 4.2.1 Network Model -- 4.2.2 RAN Slicing -- 4.2.3 Service Requirements. ; 4.2.4 Handoff Cost -- 4.3 Problem Formulation -- 4.3.1 Problem Statement -- 4.3.2 Markov Decision Process Modeling for Device Association -- 4.3.2.1 State -- 4.3.2.2 Action -- 4.3.2.3 Transition Probability -- 4.3.2.4 Reward -- 4.4 Hybrid Federated Deep Reinforcement Learning for Device Association -- 4.4.1 Framework of HDRL -- 4.4.1.1 DRL on Smart Devices -- 4.4.1.2 Horizontal Model Aggregation (hDRL) Level -- 4.4.1.3 Vertical Model Aggregation (vDRL) Level -- 4.4.2 Algorithm of Horizontal Model Aggregation -- 4.4.2.1 DDQN for Training Local Model -- 4.4.2.2 Update Models -- 4.4.3 Algorithm of Vertical Model Aggregation -- 4.4.4 HDRL Algorithm for Device Association -- 4.4.5 Convergence Analysis -- 4.5 Numerical Results -- 4.5.1 Simulation Settings -- 4.5.2 Numerical Results and Discussions -- 4.6 Conclusion -- Acknowledgment -- References -- Chapter 5 Deep Federated Learning Based on Knowledge Distillation and Differential Privacy -- 5.1 Introduction -- 5.2 Related Work -- 5.3 System Model -- 5.3.1 Security Model -- 5.4 The Implementation Details of the Proposed Strategy -- 5.4.1 Security Analysis -- 5.5 Performance Evaluation -- 5.5.1 Experimental Environment -- 5.5.2 Experimental Results -- 5.6 Conclusions -- Bibliography -- Chapter 6 Federated Learning‐Based Beam Management in Dense Millimeter Wave Communication Systems -- 6.1 Introduction -- 6.1.1 Prior Work -- 6.1.2 Contributions -- 6.2 System Model -- 6.3 Problem Formulation and Analysis -- 6.4 FL‐Based Beam Management in UDmmN -- 6.4.1 Markov Decision Process Model -- 6.4.2 FL‐Based Beam Management -- 6.4.2.1 Data Cleaning -- 6.4.2.2 Model Training -- 6.4.3 User Association in UDmmN -- 6.4.3.1 Downlink Measurements -- 6.4.3.2 User Perception -- 6.4.3.3 Multiple Association -- 6.5 Performance Evaluation -- 6.5.1 Comparison with BFS and EDB -- 6.5.2 Comparison with BMDL and BMCL. ; 6.6 Conclusions -- Bibliography -- Chapter 7 Blockchain‐Empowered Federated Learning Approach for An Intelligent and Reliable D2D Caching Scheme -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Learning‐Based D2D Caching Schemes -- 7.2.2 Blockchain‐Enabled D2D Caching Schemes -- 7.3 System Model -- 7.3.1 D2D Network -- 7.3.2 Content Caching Schemes -- 7.3.3 Transmission Latency -- 7.4 Problem Formulation and DRL‐Based Model Training -- 7.4.1 Problem Formulation -- 7.4.1.1 Action -- 7.4.1.2 State -- 7.4.1.3 Reward and Return -- 7.4.2 DRL‐Based Local Model Training -- 7.5 Privacy‐Preserved and Secure BDRFL Caching Scheme Design -- 7.5.1 Task and Requirements Publication -- 7.5.2 Appropriate UE Selection -- 7.5.3 Local Model Training -- 7.5.4 Area Model Update and Recording -- 7.5.5 Global Model Update and Recording -- 7.6 Consensus Mechanism and Federated Learning Model Update -- 7.6.1 Double‐Layer Blockchain Consensus Mechanism -- 7.6.2 FL Area Model Update in Subchain Layer -- 7.6.3 FL Global Model Update in Mainchain Layer -- 7.7 Simulation Results and Discussions -- 7.7.1 Simulation Setting -- 7.7.2 Numerical Results -- 7.8 Conclusion -- References -- Chapter 8 Heterogeneity‐Aware Dynamic Scheduling for Federated Edge Learning -- 8.1 Introduction -- 8.2 Related Works -- 8.3 System Model for FEEL -- 8.3.1 Flow of FEEL with Scheduling -- 8.3.2 Delay and Energy Model in FEEL -- 8.3.2.1 Delay Model -- 8.3.2.2 Energy Model -- 8.4 Heterogeneity‐Aware Dynamic Scheduling Problem Formulation -- 8.4.1 Convergence of FEEL with Scheduling -- 8.4.2 Scheduling Policy with Sequential Transmission -- 8.4.3 Problem Formulation -- 8.5 Dynamic Scheduling Algorithm Design and Analysis -- 8.5.1 Benchmark: R‐Round Lookahead Algorithm -- 8.5.2 DISCO: Dynamic Scheduling Algorithm -- 8.5.3 Algorithm Analysis, Complexity Reduction, and Implementation Discussion. ; 8.5.3.1 Algorithm Analysis -- 8.5.3.2 Complexity Reduction -- 8.5.3.3 Implementation Discussion -- 8.6 Evaluation Results -- 8.6.1 Parameter Settings -- 8.6.2 Numerical Results -- 8.6.3 Experimental Results -- 8.7 Conclusions -- 8.A.1 Proof of Theorem 8.2 -- 8.A.2 Proof of Theorem 8.3 -- 8.A.2.1 Feasibility Proof -- 8.A.2.2 Optimality Proof -- References -- Chapter 9 Robust Federated Learning with Real‐World Noisy Data -- 9.1 Introduction -- 9.1.1 Work Prior to FedCorr -- 9.2 Related Work -- 9.2.1 Federated Methods -- 9.2.2 Local Intrinsic Dimension (LID) -- 9.2.2.1 Estimation of LID -- 9.3 FedCorr -- 9.3.1 Preliminaries -- 9.3.1.1 Data Partition -- 9.3.1.2 Noise Model -- 9.3.1.3 LID Scores for Local Models -- 9.3.2 Federated Preprocessing Stage -- 9.3.2.1 Client Iteration and Fraction Scheduling -- 9.3.2.2 Mixup and Local Proximal Regularization -- 9.3.2.3 Identification of Noisy Clients and Noisy Samples -- 9.3.3 Federated Fine‐Tuning Stage -- 9.3.4 Federated Usual Training Stage -- 9.4 Experiments -- 9.4.1 Experimental Setup -- 9.4.1.1 Baselines -- 9.4.1.2 Implementation Details -- 9.4.2 Comparison with State‐of‐the‐Art Methods -- 9.4.2.1 IID Settings -- 9.4.2.2 Non‐IID Settings -- 9.4.2.3 Combination with Other FL Methods -- 9.4.2.4 Comparison of Communication Efficiency -- 9.4.3 Ablation Study -- 9.5 Further Remarks -- Bibliography -- Chapter 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 -- 10.3.1 Salient Features -- 10.3.2 Heavy‐Tailed Interference -- 10.4 Convergence Analysis -- 10.4.1 Preliminaries -- 10.4.2 Convergence Rate of AirFL -- 10.4.3 Key Observations -- 10.5 Numerical Results -- 10.6 Conclusion -- Bibliography -- Chapter 11 Federated Edge Learning for Massive MIMO CSI Feedback -- 11.1 Introduction -- 11.2 System Model. ; 11.2.1 Channel Model and Signal Model.
• Titolo autorizzato: Federated Learning for Future Intelligent Wireless Networks
• ISBN: 1-119-91390-X; 1-119-91392-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910840873603321
• Lo trovi qui: Univ. Federico II