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|a Sun, Yao.
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|a Federated Learning for Future Intelligent Wireless Networks
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|a Newark :
|b John Wiley & Sons, Incorporated,
|c 2023.
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|a 1 online resource (317 p.)
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|a Description based upon print version of record.
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|a 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
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|a 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
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|a 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
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|a 3.5.1 Case 1: Sufficient Communication Resources and Computing Resources
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|a You, Chaoqun.
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|a Feng, Gang.
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|a Zhang, Lei.
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|i Print version:
|a Sun, Yao
|t Federated Learning for Future Intelligent Wireless Networks
|d Newark : John Wiley & Sons, Incorporated,c2023
|z 9781119913894
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| 856 |
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|u https://ebookcentral.proquest.com/lib/ucb/detail.action?docID=30977884
|z Full Text (via ProQuest)
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| 880 |
0 |
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|6 505-00
|a 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
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|6 505-00
|a 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
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