Federated Learning for IoT Applications

Preface
Contents
Introduction to Federated Learning
1 Introduction
1.1 Federated Learning
1.2 Example of Application of Federated Learning
2 IoT and Federated Learning
2.1 Concept of Federated Learning
2.2 Internet of Things (IoT)
2.3 Federated Learning and IoT
3 Security: Federated Learning in IoT
3.1 Federated Machine Learning for Secure IoT
3.2 Federated Deep Learning in IoT
4 Data Management and Resource Management
5 Applications
5.1 Federated Learning Application in Automotive Industry
5.2 Smart Medical Industry
References
Federated Learning for IoT Devices
1 Introduction
2 IoT Devices
3 Federated Setting
4 Connection Establishment
5 Actor Programming Model
6 Challenges and Threats in FL
7 Conclusion
References
Personalized Federated Learning
1 Introduction
1.1 Work-Related Federal Learning with IoT Devices
2 Internet of Things Architecture
2.1 Architectures of Three to Five Layers
2.2 Architectures Centered on the Cloud and Fog
2.3 Social IoT
2.3.1 Basic Components
Object Recognition
Metadata
Security Controls
Domain Discovery
Partnership Management
Service Composition
2.3.2 Architecture that is Representative
3 In the Internet of Things, Privacy Is Critical
3.1 Motivation
3.2 Challenges
3.2.1 Tracking and Profiling
3.2.2 Tracking and Localization
3.2.3 Data Transfer that is Reliable
3.3 Discussion and Existing Approach
4 What Is Federated Learning, and How Does It Work?
4.1 Federated Learning’s Obstacles
4.1.1 Noise Versus Precision Trade-Off
4.1.2 System and Statistical Heterogeneity
4.1.3 Communication Bottlenecks
4.1.4 Poisoning
4.1.5 Trade-Off Between Reliability and Safety
4.2 Problems Solved by Federated Learning Have the Following Characteristics
4.2.1 First Characteristic
4.2.2 The Second Property
4.2.3 Third Property
4.3 Federated Machine Learning Meets Intellectual IoT
4.3.1 Intelligent IoT Services Utilizing Federated Machine Learning
4.3.2 During Each Learning Round, the Following Three Phases Are Performed in Order
Platform Selection
Local Model Upload
Global Model Download
4.3.3 The Following Are Some of the Benefits of Federated Deep Learning in Intelligent IoT Networks
User Privacy and Data Protection Are Preserved
Collective Training
Reduced Network Latency
4.3.4 Federated Machine Learning Enables Key Intelligent IoT Applications
Self-Driving Cars
Unmanned Aerial Vehicles (UAVs)
Healthcare
Robotics
Supply Chain Finance
4.3.5 Federated Machine Learning’s Coordination Problem
4.4 Artificial Intelligence (AI) as a Defense Mechanism and IoT Security Requires
4.5 Federated Machine Learning of Over-the-Air Computing
4.5.1 Over-the-Air Computation Rules
5 Conclusions
References
Federated Learning for an IoT Application
1 Introduction
2 Federated Learning
2.1 Edge Learning in an IoT Environment
2.2 Learning for Decentralized IoT Applications
2.3 Federated Learning for an IoT Application
3 Challenges in FL
3.1 Inability to Inspect Training Data
3.2 Participation of Big Players
3.3 Cheating Behavior
3.4 Communication Failure
3.5 Low Participation
4 Threats and Possible Countermeasure
4.1 Poisoning Attack
4.2 Backdoor Attack
4.3 Equation-Solving Attack
4.4 Extreme Poisoning Attack
5 Conclusion
References
Some Observations on the Behaviour of Federated Learning
1 Introduction
2 Federated Learning
3 Different Architectures of FL
3.1 Sample-Based Federated Learning (SBFL)
3.2 Feature-Based Federated Learning (FBFL)
3.3 Federated Transfer Learning (FTL)
4 Federated Learning Applied to IoT Device
5 Conclusion
References
Federated Learning with Cooperating Devices: A Consensus Approach
1 Introduction
2 Blockchain
3 Agreement-Based United Averaging
4 Solidifying Federated Averaging with Consensus. A Case Study: A Contextual Analysis
4.1 Transportation: Self-Driving Vehicles
4.2 Industry 4.0: Shrewd Hoarding
4.2.1 Medication: Advanced Flourishing
4.3 Affirmations Against External Malicious Actors
4.3.1 Auditing the Iterates and Final Model
4.3.2 Training with Central Differential Privacy
4.3.3 Repeated Analyses Over Evolving Data
4.3.4 Preventing Model Theft and Misuse
4.4 Challenges: Communication Channels, Sybil Attacks, and Selection
4.5 Limitations of Existing Solutions
4.5.1 Limitations of Existing Solutions
4.5.2 Behavioral Research to Elicit Privacy Preferences
5 Force to Attacks and Failures
5.1 Conclusions and Open Issues
References
A Prospective Study of Federated Machine Learning in Medical Science
1 Introduction
1.1 Artificial Intelligence In Medical And Healthcare Applications
1.1.1 Internet of Things in Smart Healthcare System
1.2 Artificial Intelligence Devices
1.2.1 NLP in Healthcare
1.3 Modules in Smart Healthcare System
2 Artificial Intelligence Medical Systems
2.1 Characteristics of Artificial Intelligence Medical Systems
3 Types of Artificial Intelligence Medical Systems
3.1 Artificial Intelligence CAD
3.2 Artificial Intelligence Surgical Robot
4 Conclusion
References
Communication-Efficient Federated Learning in Wireless-Edge Architecture
1 Introduction
2 Ubiquitous Artificial Intelligence
3 Federated Learning
3.1 Categories of Federated Learning
3.2 Federated Learning Performance Indicators
3.2.1 Delay
3.2.2 Energy
3.2.3 Reliability
3.2.4 Massive Connectivity
3.3 Motivating Factors of Federated Learning for 6G
3.3.1 Resource Management
3.3.2 Communication Efficient Artificial Intelligence
3.3.3 User Behavior Predictions
3.3.4 Signal Detection and Channel Estimation
3.3.5 Flexible Network Architecture
4 Road Map to 6G
4.1 Zero Generation
4.2 First Generation
4.3 Second Generation
4.3.1 2.5G
4.4 Third Generation
4.5 Fourth Generation
4.6 Fifth Generation
4.7 Sixth Generation
5 Convergence of Ubiquitous Artificial Intelligence, Federated Learning, and 6G
5.1 The Most Common AI Techniques Are Machine Learning (Supervised Learning, Unsupervised Learning and Reinforcement Learning) and Deep Learning
5.1.1 Supervised Learning
5.1.2 Unsupervised Learning
5.1.3 Reinforcement Learning
5.1.4 Deep Learning
5.2 Potential of Federated Learning to Support 6G
5.2.1 Massive Ultra-Reliable, Low-Latency Communications (mURLLC)
5.2.2 Scalable Architecture
5.2.3 Human-Centric Services
6 Challenges and Future Scope [2, 3, 9]
7 Conclusion
References
Communication-Efficient Federated Learning
1 Introduction
1.1 Why Is Federated Learning Important?
1.1.1 Putting Federated Learning into Reality
1.2 Three Phases for FL-Based Architecture
1.2.1 A device’s Service Requests May be Evaluated in the First Step of Initialization to See whether it Can Link with the Nearby Cloud to Train an ML Model through 6G
1.3 The 6G Network Architecture
1.4 From Softwarization to Ntelligentization of Networks
1.5 6G for Applications in Artificial Intelligence
1.5.1 Intelligence around Applications
1.6 Deep Sea and Space Communication
1.7 Networks that Are Energy Consuming
2 Overview and Preliminaries
2.1 High-Capacity Networking
2.2 Energy Efficiency Improvements
2.3 High Levels of Safety and Privacy
2.4 Superior Intelligence
2.4.1 Operational Intelligence
2.5 Scenarios in Typical Use
3 Federated Learning
4 Primary Difficulties in Federated Learning for 6G
4.1 Expensive Communication Is a Challenge
4.2 Concerns over Privacy Pose a Challenge
4.3 Effective Issues Are a Challenge
5 For 6G, Advanced Federated Learning Methods Are Accessible
5.1 Federated Learning with High Communication Efficiency for 6G
5.1.1 Effective Communication FL
5.1.2 Effective Communication FL
5.1.3 Model Convergence Acceleration
5.1.4 Reducing Connectivity Overhead
5.2 6G Safe Federated Learning
5.2.1 Algorithm for Robust Aggregation
5.2.2 Detection Mechanism of High Efficacy
5.2.3 Reliable Reputation Management
5.2.4 Neural Network Pruning
5.2.5 Compression of Gradients
5.3 For 6G, Effective Federated Learning Is Important
5.3.1 Effective Training
Federated Parallelization
Federated Distillation
5.3.2 Efficient Inference
Pruning
Weight Sharing
5.4 Federated Learning May be Explained
6 Future Research Directions and Open Research Topics
6.1 Federated Learning that Is Reliable
6.1.1 Federated Learning with Privacy Enhancement
6.1.2 Federated Learning with Improved Security
6.1.3 Fair Federated Education
6.2 Federated Learning that Is both Efficient and Effective
6.3 The Direction of Incentive Federated Learning
6.4 Steps toward Customized Federated Learning
7 Conclusion
References
Federated Learning Using Tensor Flow
1 Introduction
2 Federated Learning
2.1 Federated Learning (FL) API
3 Federated Core
4 Conclusion
References
Cyber Security and Privacy of Connected and Automated Vehicles (CAVs)-Based Federated Learning: Challenges, Opportunities, and Open Issues
1 Introduction
2 Review of Federated Learning
3 Cyber Security in Vehicular Network
4 Federated Learning (FL) Algorithm Design
5 Federated Machine Learning (ML) Method Layout
6 Conclusion
References
Security Issues and Solutions for Healthcare Informatics
1 Introduction
2 Blockchain Technology-Based Solution for Healthcare Informatics
3 Pseudonymization of Healthcare Data
4 Access Control
4.1 DAC (Discretionary Access Control)
4.2 MAC (Mandatory Access Control)
4.3 RBAC (Role-Based Access Control)
4.4 Access Control Mechanism Suitable for the Healthcare System
5 E-Health Model in Serbia
6 Anonymous e-Prescriptions
7 Smart Card-Enabled e-Prescriptions System [6]
8 Patient-Controlled Pseudonym-Based EHR (PcPbEHR)
9 Conclusion
References
Federated Learning: Challenges, Methods, and Future Directions
1 Introduction
2 Overview of Federated Learning
2.1 Definition and Working of Federated Learning
2.2 Types of Federated Learning
2.3 Architecture of Horizontal Federated Learning
2.3.1 Client-Server Architecture
2.3.2 Peer-Peer Architecture
2.4 Applications
3 Life Cycle of Federated Learning Model
4 Challenges in Federated Learning
4.1 Expensive Communication
4.2 Systems Heterogeneity
4.3 Number of Clients
4.4 Scalability
4.5 Restrictions of Battery and Resource
4.6 Security
5 Future Directions
6 Conclusion
References
Quantum Federated Learning for Wireless Communications
1 Introduction
1.1 Centralized Quantum Learning in Federated Mode
1.2 Decentralized Quantum Federated Learning
1.3 Heterogeneous Federated Learning
2 Main Features of Quantum Federated Learning
2.1 Learning Iterative Nature
2.2 Non-iid Data
3 Hyper-Parameter Algorithm
3.1 Topology Related to Network
3.2 Quantum Federated Learning Parameters
4 Quantum Federated Learning Variations
4.1 Quantum Federated Stochastic Gradient Descent (FedSGD)
4.2 Quantum Federated Averaging
5 Limitations on Technical Ground
6 Quantum Federated Learning Properties
6.1 Design Privacy
6.2 Personalization
6.3 Legal Issues of Quantum Federated Learning
7 Current Research Topics
8 Cases in Use
8.1 Transportation: Self-Driving Cars
8.2 Smart Manufacturing: Industry 4.0
8.3 Medicine: Digital Health
8.4 Wireless Communications
References
Federated Machine Learning with Data Mining in Healthcare
1 Introduction
1.1 Legal Issues of Patient History
1.2 Overview of Machine Learning and Data Mining
1.3 Different Types of Machine Learning
2 Federated Machine Learning
3 Architecture of Federated Machine Learning
3.1 Privacy Models of Federated Machine Learning
4 Literature Review
5 Limitations and Solutions
6 Conclusion
References
Federated Learning for Data Mining in Healthcare
1 Introduction
2 Federated Learning
3 Healthcare
4 Data-Mining-Approach
4.1 Preprocessing
4.2 Support Vector Machine
4.3 Neural Networks
4.4 Gaussian Mixture Models
4.5 Hidden Markov Models
4.6 Rule-Based Methods
4.7 Statistical Tools
4.8 Frequency Domain/Wavelet Analysis
5 Applications
6 Conclusion
References
Index