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 ฮท
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