Description
Book SynopsisPart 1: Introduction and Overview.- 1. Introduction and overview.- 1.1. Overview of federated edge learning (FEEL).- 1.2. Learning models and algorithms of FEEL.- 1.3. Motivation and challenges of FEEL.- 1.4. Organization.- Part 2: Algorithms.- 2. First-order optimization for FEEL.- 2.1. Background and motivation.- 2.2. Federated first-order optimization model and algorithm.- 2.3. Sparse and low-rank optimization for FEEL.- 2.4. Simulations and discussions.- 2.5. Summary.- 3. Second-order optimization for FEEL.- 3.1. Background and motivation.- 3.2. Federated second-order optimization model and algorithm.- 3.3. Convergence analysis.- 3.4. System optimization.- 3.5. Simulations and discussions.- 3.6. Summary.- 4. Zeroth-order optimization for FEEL.- 4.1. Background and motivation.- 4.2. Federated zeroth-order optimization model and algorithm.- 4.3. Convergence analysis.- 4.4. Over-the-air federated zeroth-order optimization.- 4.5. Simulations and discussions.- 4.6. Summary.- Part 3: Architectures.- 5. Reconfigurable intelligent surface assisted FEEL.- 5.1. Background and motivation.- 5.2. Communication and learning models.- 5.3. Convergence analysis and problem formulation.- 5.4. Alternating optimization algorithm design.- 5.5. GNN-based learning algorithm design.- 5.6. Simulations and discussions.- 5.7. Summary.- 6. Unmanned aerial vehicle assisted FEEL.- 6.1. Background and motivation.- 6.2. Communication and learning models.- 6.3. Convergence analysis and problem formulation.- 6.4. Joint device scheduling, time allocation, and trajectory design.- 6.5. Simulations and discussions.- 6.6. Summary.- 7. FEEL over multi-cellwireless networks.- 7.1. Background and motivation.- 7.2. Communication and learning models.- 7.3. Convergence analysis and problem formulation.- 7.4. Cooperative optimization for multi-cell FEEL.- 7.5. Simulations and discussions.- 7.6. Summary.- Part 4: Trustworthiness.- 8. Differentially-private FEEL.- 8.1. Background and motivation.- 8.2. System model.- 8.3. Performance analysis and privacy preserving mechanism.- 8.4. Two-step alternating low-rank optimization.- 8.5. Simulations and discussions.- 8.6. Summary.- 9. Trustworthy FEEL via blockchain.- 9.1. Background and motivation.- 9.2. System model.- 9.3. Latency analysis and problem formulation.- 9.4. TD3 based resource allocation.- 9.5. Simulations and discussions.- 9.6. Summary.- Part 5: Conclusions and Future Directions.- 10. Conclusions and future directions.- 10.1. Conclusions.- 10.2. Future directions.
