Federated learning over wireless edge networks / / Wei Yang Bryan Lim, [and four others]

• Record Nr.: UNISA996490358403316
• Autore: Lim Wei Yang Bryan
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 3-031-07838-1
• Descrizione fisica: 1 online resource (175 pages)
• Collana: Wireless Networks
• Disciplina: 929.374
• Soggetti: Edge computing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Contents -- List of Figures -- List of Tables -- 1 Federated Learning at Mobile Edge Networks: A Tutorial -- 1.1 Introduction -- 1.2 Background and Fundamentals of Federated Learning -- 1.2.1 Federated Learning -- 1.2.2 Statistical Challenges of FL -- 1.2.3 FL Protocols and Frameworks -- 1.2.4 Unique Characteristics and Issues of FL -- 1.3 Communication Cost -- 1.3.1 Edge and End Computation -- 1.3.2 Model Compression -- 1.3.3 Importance-Based Updating -- 1.4 Resource Allocation -- 1.4.1 Worker Selection -- 1.4.2 Joint Radio and Computation Resource Management -- 1.4.3 Adaptive Aggregation -- 1.4.4 Incentive Mechanism -- 1.5 Privacy and Security Issues -- 1.5.1 Privacy Issues -- 1.5.1.1 Information Exploiting Attacks in Machine Learning: A Brief Overview -- 1.5.1.2 Differential Privacy-Based Protection Solutions for FL Workers -- 1.5.1.3 Collaborative Training Solutions -- 1.5.1.4 Encryption-Based Solutions -- 1.5.2 Security Issues -- 1.5.2.1 Data Poisoning Attacks -- 1.5.2.2 Model Poisoning Attacks -- 1.5.2.3 Free-Riding Attacks -- 1.6 Applications of Federated Learning for Mobile Edge Computing -- 1.6.1 Cyberattack Detection -- 1.6.2 Edge Caching and Computation Offloading -- 1.6.3 Base Station Association -- 1.6.4 Vehicular Networks -- 1.7 Conclusion and Chapter Discussion -- 2 Multi-dimensional Contract Matching Design for Federated Learning in UAV Networks -- 2.1 Introduction -- 2.2 System Model and Problem Formulation -- 2.2.1 UAV Sensing Model -- 2.2.2 UAV Computation ; Model -- 2.2.3 UAV Transmission Model -- 2.2.4 UAV and Model Owner Utility Modeling -- 2.3 Multi-dimensional Contract Design -- 2.3.1 Contract Condition Analysis -- 2.3.2 Conversion into a Single-Dimensional Contract -- 2.3.3 Conditions for Contract Feasibility -- 2.3.4 Contract Optimality -- 2.4 UAV-Subregion Assignment -- 2.4.1 Matching Rules.; 2.4.2 Matching Implementation and Algorithm -- 2.5 Performance Evaluation -- 2.5.1 Contract Optimality -- 2.5.2 UAV-Subregion Preference Analysis -- 2.5.3 Matching-Based UAV-Subregion Assignment -- 2.6 Conclusion and Chapter Discussion -- 3 Joint Auction-Coalition Formation Framework for UAV-Assisted Communication-Efficient Federated Learning -- 3.1 Introduction -- 3.2 System Model -- 3.2.1 Worker Selection -- 3.2.2 UAV Energy Model -- 3.2.2.1 Flying Energy -- 3.2.2.2 Computational Energy -- 3.2.2.3 Communication Energy -- 3.2.2.4 Hovering Energy -- 3.2.2.5 Circuit Energy -- 3.3 Coalitions of UAVs -- 3.3.1 Coalition Game Formulation -- 3.3.2 Coalition Formation Algorithm -- 3.4 Auction Design -- 3.4.1 Buyers' Bids -- 3.4.2 Sellers' Problem -- 3.4.3 Analysis of the Auction -- 3.4.4 Complexity of the Joint Auction-Coalition Algorithm -- 3.5 Simulation Results and Analysis -- 3.5.1 Communication Efficiency in FL Network -- 3.5.2 Preference of Cells of Workers -- 3.5.3 Profit-Maximizing Behavior of UAVs -- 3.5.4 Allocation of UAVs to Cells of Workers -- 3.5.5 Comparison with Existing Schemes -- 3.6 Conclusion and Chapter Discussion -- 4 Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- 4.1 Introduction -- 4.2 System Model and Problem Formulation -- 4.2.1 System Model -- 4.2.2 Lower-Level Evolutionary Game -- 4.2.3 Upper-Level Deep Learning Based Auction -- 4.3 Lower-Level Evolutionary Game -- 4.3.1 Evolutionary Game Formulation -- 4.3.2 Worker Utility and Replicator Dynamics -- 4.3.3 Existence, Uniqueness, and Stability of the Evolutionary Equilibrium -- 4.4 Deep Learning Based Auction for Valuation of Cluster Head -- 4.4.1 Auction Formulation -- 4.4.2 Deep Learning Based Auction for Valuation of Cluster Heads -- 4.4.3 Monotone Transform Functions -- 4.4.4 Allocation Rule -- 4.4.5 Conditional Payment Rule.; 4.4.6 Neural Network Training -- 4.5 Performance Evaluation -- 4.5.1 Lower-Level Evolutionary Game -- 4.5.1.1 Stability and Uniqueness of the Evolutionary Equilibrium -- 4.5.1.2 Evolutionary Equilibrium Under Varying Parameters and Conditions -- 4.5.2 Upper-Level Deep Learning Based Auction -- 4.5.2.1 Evaluation of the Deep Learning Based Auction -- 4.6 Conclusion and Chapter Discussion -- 5 Conclusion and Future Works -- References -- Index.