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Decentralized Collaborative Learning for Data Privacy and Security

Edited by Bipin Kumar Rai, Rupa Rani, and Chin-Shiuh Shieh
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394464685 | Hardcover |
562 pages
Price: $225 USD

One Line Description
Master the future of secure technology with this essential guide, which delivers a practical, forward-looking blueprint for combining AI, blockchain, and advanced cryptography to build powerful, decentralized systems without compromising data privacy.

Description
As AI systems grow more powerful and data more valuable, the tension between collaborative intelligence and individual privacy has never been more urgent. This book confronts this challenge head-on, offering a comprehensive and forward-looking exploration of how federated learning, blockchain technology, and advanced cryptographic techniques can be combined to build AI systems that are powerful and trustworthy. From foundational concepts in distributed machine learning and data sovereignty to cutting-edge topics such as zero-knowledge proofs, homomorphic encryption, and quantum-resistant privacy solutions, this book is structured to serve both learners and practitioners. The book moves deliberately from theory to practice, establishing core principles before tackling complex architectures, consensus mechanisms, smart contract governance, and incentive models that make decentralized AI networks viable at scale. Real-world applications anchor the technical content throughout, with dedicated chapters examining privacy-preserving fraud detection in finance, blockchain-AI collaboration in healthcare, and intelligent infrastructure in smart cities and IoT ecosystems. Supported by case studies, architectural diagrams, and practical guidance on tools, it is an essential resource for anyone working at the intersection of AI, blockchain, and data privacy to shape the secure, decentralized intelligent systems of tomorrow.

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Author / Editor Details
Bipin Kumar Rai, PhD is a Professor in the Department of Computer Science and Engineering, Dayanand Sagar University, Bangalore. With more than 21 years of experience, he has published more than 70 research papers in international journals and conferences, seven books, and five patents. His interests include information security, machine learning, and blockchain.

Rupa Rani, PhD is an Assistant Professor in the Department of Computer Science and Engineering at Ajay Kumar Garg Engineering College, Ghaziabad, India. With over a decade of teaching experience, she has published more than 20 research papers in reputed international journals and conferences. Her research interests include data science, artificial intelligence, machine learning, and cyber security.

Chin-Shiuh Shieh, PhD is a Professor in the Department of Electronic Engineering at the National Kaohsiung University of Science and Technology, Kaohsiung. He has more than 200 publications to his credit, including books, chapters, and journal articles. His research interests include wireless networks and handover techniques.

Table of Contents
Preface
1. Cross-Border Healthcare Data Sharing Using Blockchain and Federated Learning
Bipin Kumar Rai, Chin-Shiuh Shieh and Anoop Kumar Srivastava
1.1 Introduction
1.2 Related Work
1.3 Role of Blockchain in Cross-Border Federated Learning in Healthcare
1.4 Major Research Trends
1.5 Common Datasets and Benchmarks
1.6 Prevailing Challenges and Open Research Gaps
1.6.1 Privacy Leakage through Model Updates
1.6.2 Scalability and Performance Limitations
1.6.3 Heterogeneity of Data, Systems, and Participants
1.7 Promising Directions for Future Work
1.8 Practical Notes for Deployment
1.9 Conclusion
References
2. The Role of Artificial Intelligence in Privacy-Aware Data Collaboration
Kumar Dilip, Bipin Kumar Rai and Yashwant Shukla
2.1 Introduction
2.2 Background and Primitives
2.2.1 Federated Learning—Overview and Objectives
2.2.2 Statistical Privacy: Differential Privacy
2.2.3 Secure Multi-Party Computation
2.2.4 Homomorphic Encryption
2.2.5 Blockchain-Based Collaborative Governance
2.3 Taxonomy of Privacy-Aware Collaborative AI
2.3.1 Centralized Training with Differential Privacy
2.3.2 Federated Learning with Secure Aggregation
2.3.3 Federated Learning with Differential Privacy (FL+DP)
2.3.4 Hybrid Federated Learning with HE/SMPC
2.3.5 Peer-to-Peer (P2P) Collaboration with Blockchain Governance
2.3.6 Encrypted Inference via Homomorphic Encryption
2.4 Mathematical Formulation: Privacy–Utility–Efficiency Trade-Off
2.4.1 Federated Optimization with Privacy Noise
2.4.2 Secure Aggregation Correctness and Adversarial Model
2.4.3 End-to-End Risk Function
2.5 System Design Patterns and Examples
2.6 Privacy-Preserving Explainable AI
2.7 Legal, Ethical, and Governance Consideration
2.7.1 Legal Landscape, Key Instruments and Implications
2.7.2 Ethical Principles and Translation to Practice
2.7.3 Governance Structures for Collaborative AI
2.7.4 Accountability, Auditability, and Assurance
2.7.5 Emerging Gaps and Research Needs
2.8 Performance, Efficiency, and System Engineering
2.8.1 Key System Engineering Concerns in Collaborative AI
2.8.2 Performance-Efficiency Trade-Offs in Privacy-Aware AI Collaboration
2.8.3 Engineering Best Practice Guidelines
2.8.4 Emerging Research and Engineering Frontiers
2.9 Evaluation Metrics and Benchmarks
2.10 Open Problem and Research Agenda
2.10.1 Key Challenging Areas in Privacy Aware AI Collaboration
2.10.2 Proposed Research Agenda
2.11 Conclusion
References
3. Understanding Data Privacy in the Age of Distributed AI
Ayush Tripathi, Prashant Upadhyay, Pawan Kumar and Sinem Alturjman
3.1 Introduction
3.2 Distributed Intelligence and the Shift toward Privacy-Preserving AI
3.3 The Privacy Problems in the Contemporary Distributed AI World
3.4 Generalized Privacy-Preserving Techniques That Support Secure Distributed AI
3.5 Distributed Artificial Intelligence Blockchain Foundations of Trust and Transparency
3.6 Edge and Fog Computing As the Strengths of Distributed AI
3.7 Regulatory Landscapes and Ethical Expectations in Distributed Artificial Intelligence
3.8 Future Scope
3.9 Conclusion
References
4. Blockchain Technology for Secure and Ethical AI-Driven Healthcare
Jaishree Jain, Updesh Kumar Jaiswal, Shraddha Mishra and Mani Dublish
4.1 Introduction
4.1.1 Background Study
4.1.2 Importance of Research
4.2 Literature Review
4.2.1 Criteria for Selection
4.2.2 Evaluation of Quality
4.2.3 Selection Outcomes
4.3 Artificial Intelligence Possible Attacks
4.3.1 Vulnerabilities in AI
4.3.2 Algorithms and Classifiers
4.3.2.1 Backdoor/Trapdoor Attack
4.3.3 Possible Solutions
4.4 Blockchain-Based AI Healthcare Solution
4.4.1 NLP-Based Medical Services
4.4.2 Healthcare Based on Computer Vision
4.4.2.1 Constructed Structure
4.4.3 Healthcare with Acoustic AI
4.5 Blockchain-Based AI Healthcare Methods Results
4.5.1 Insufficiently Stable Computational Environment
4.5.2 A Lack of Interoperability and a Standardized IT System
4.5.3 Blockchain Developments
4.5.3.1 The Blockchain of Quantum
4.5.3.2 Encryption That is Homeopathic
4.6 Conclusion
References
5. Federated Learning and Blockchain: A Collaborative Paradigm for Secure and Decentralized AI
Sonam Gupta and Pradeep Gupta
5.1 Introduction
5.2 Fundamental Principles and Architecture
5.2.1 Core Concepts
5.2.2 Architectural Components
5.2.3 Communication Protocols
5.3 Privacy and Security Mechanisms
5.3.1 Privacy Preservation Techniques
5.3.2 Security Challenges and Solutions
5.3.3 Trust and Verification
5.4 Algorithmic Foundations
5.4.1 Federated Averaging Algorithm
5.4.2 Handling Non-IID Data
5.4.3 Advanced Optimization Techniques
5.5 Blockchain Integration in Federated Learning
5.5.1 Rationale for Integration
5.5.2 Architectural Synergy
5.5.3 Security and Trust Reinforcement
5.5.4 Applications and Emerging Directions
5.6 Applications and Use Cases
5.6.1 Healthcare Applications
5.6.2 Financial Services
5.6.3 Internet of Things and Edge Computing
5.7 Technical Challenges and Solutions
5.7.1 Communication Efficiency
5.7.2 System Heterogeneity
5.7.3 Scalability and Performance
5.8 Evaluation Metrics and Benchmarks
5.8.1 Performance Metrics
5.8.2 Privacy Evaluation
5.8.3 Benchmark Datasets and Frameworks
5.9 Future Directions and Emerging Trends
5.9.1 Integration with Emerging Technologies
5.9.2 Regulatory and Ethical Considerations
5.10 Conclusion
References
6. Smart Contracts-Based Autonomous Governance System for Smart City
Pawan Kumar, Rupa Rani, Jyoti Rani, Santosh Kumar Mishra and Shivpratap Singh Kushwah
6.1 Introduction
6.2 Smart City
6.3 Challenges in Smart City
6.3.1 Collaborative Services
6.3.2 Security Challenges
6.3.3 Security Requirement
6.4 Smart Contracts
6.4.1 Benefits of Smart Contracts
6.4.2 Disadvantages of Smart Contracts
6.5 Smart Contracts in Smart City
6.5.1 Key Applications of Smart Contracts in Smart Cities
6.5.2 Challenges of Implementing Smart Contracts in Smart City
6.5.3 Advantages for Implementing Smart Contracts in Smart City
6.5.4 Disadvantages of Implementing Smart Contracts in Smart City
References
7. Securing Legal Practice in Nigeria: Integrating AI and Blockchain for Cyber Resilience
Udebuani, Chidiogo Mercy and Rajesh Prasad
7.1 Introduction
7.1.1 Overview
7.1.1.1 The Threat Landscape: Why Law Firms are Targets
7.1.1.2 Core Challenges Facing the Nigerian Legal Industry
7.1.1.3 Case Illustrations and Recent Developments (Selected)
7.1.1.4 Limitations of the Study
7.1.1.5 Methodology
7.2 The Role of AI and Blockchain in Enhancing Legal Practice and Cybersecurity
7.2.1 The Role of AI in Enhancing Legal Practice and Cybersecurity
7.2.2 Blockchain Applications for Trust and Resilience
7.3 Strategic Pathways for Nigeria’s Legal Sector
7.4 Nigerian Legal-Regulatory and Evidentiary Context
7.4.1 Regulatory and Institutional Context (Breakdown)
7.5 AI for Cyber-Resilient Legal Practice
7.6 Blockchain for Integrity, Auditability, and Trust
7.7 AI–Blockchain Integration: A Reference Architecture for Law Firms
7.7.1 Hybrid AI–Blockchain Cybersecurity Model for Nigeria’s Legal Industry
7.7.2 Risk, Compliance, and Professional Responsibility
7.8 Implementation Roadmap for Nigerian Law Firms
7.8.1 A Model AI Usage Policy for a Typical Nigerian Law Firm
7.9 Conclusion and Recommendations
7.9.1 Practical Recommendations for Strengthening Safety and Technology in Nigeria’s Legal Industry
7.9.2 Technological Innovations and Practical Controls for Law Firms
7.9.2.1 Foundational Controls (Low-to-Moderate Cost; Immediate Impact)
7.9.2.2 Cloud + Managed Services (Scalable; Requires Governance)
7.9.2.3 Advanced Innovations (Strategic; Longer-Term Adoption)
7.9.2.4 Legal-Technology Integration and Vendor Risk Management
7.9.3 Ethical, Professional, and Practice Considerations
7.9.4 Further Recommendations
7.10 Conclusion
References
Appendix
8. Encouraging Secure Collaboration: AI’s Function in Privacy-Aware Data Governance and Sharing
Mani Dublish, Updesh Kumar Jaiswal, Jaishree Jain and Shikha Mittal
8.1 Introduction
8.1.1 Context and Motivation
8.1.2 The Need for Secure Data Collaboration
8.1.3 Challenges in Data Sharing and Privacy
8.1.4 Role of AI in Privacy-Aware Governance
8.2 Background and Literature Review
8.3 Foundations of Privacy-Aware Data Governance
8.3.1 Definition and Principles of Data Governance
8.3.2 Privacy Principles
8.3.3 Ethical and Legal Considerations
8.3.4 Emerging Governance Frameworks
8.4 AI Techniques for Privacy-Aware Data Sharing
8.4.1 Federated Learning
8.4.2 Differential Privacy
8.4.3 Homomorphic Encryption and SMPC
8.4.4 AI for Dynamic Access Control and Policy Enforcement
8.4.4.1 Role-Based and Attribute-Based Controls
8.4.4.2 AI in Policy Adaptation
8.5 Secure Data Collaboration Architectures Powered by AI
8.5.1 Centralized vs. Decentralized Architectures
8.5.1.1 Centralized Architectures
8.5.1.2 Decentralized Architectures
8.5.2 Role of Mist, Fog, and Edge Computing
8.5.2.1 Edge Computing
8.5.2.2 Fog Computing
8.5.2.3 Mist Computing
8.5.3 Interoperability Across Systems
8.5.4 AI-Enabled Data Brokers and Middleware
8.5.4.1 Features of AI-Powered Middleware
8.6 Cross-Sector Use Cases
8.6.1 Healthcare Sector
8.6.1.1 Secure Patient Data Exchange
8.6.1.2 AI in Clinical Decision Support
8.6.2 Finance Sector
8.6.2.1 Federated Fraud Detection
8.6.2.2 AI for Risk Assessment
8.6.3 Smart Cities
8.6.3.1 Multi-Agency Data Sharing
8.6.3.2 Privacy in Urban Surveillance
8.6.4 Education and Research Sector
8.6.4.1 Collaborative Analytics in EdTech
8.7 Risks, Challenges, and Limitations
8.7.1 Model Vulnerabilities and Adversarial Threats
8.7.2 Data Bias and Fairness in AI Systems
8.7.3 Legal and Ethical Dilemmas
8.7.4 Scalability and Real-Time Limitations
8.8 Compliance and Regulatory Alignment
8.8.1 AI in GDPR, HIPAA, and CCPA Compliance
8.8.1.1 General Data Protection Regulation
8.8.1.2 Health Insurance Portability and Accountability Act
8.8.2 AI for Audit Trails and Reporting
8.8.3 Automating Privacy Impact Assessments
8.9 Future Trends and Research Directions
8.9.1 Blockchain Integration for Trust and Auditability
8.9.1.1 Key Benefits for AI-Enabled Data Sharing
8.9.2 Explainable AI in Governance
8.9.2.1 Current and Future Directions
8.9.3 Zero-Trust Architectures
8.9.4 AI and Privacy Metrics
8.10 Conclusion
References
9. Criminal Identification System Using Face Detection with Artificial Intelligence
Sunil Gupta, Tejas Singhal, Aman Kumar, Bipin Kumar Rai and Kamal Saluja
9.1 Introduction
9.2 Related Work
9.3 Objectives and Scope
9.3.1 Scope of the Research Work
9.3.2 Usages of AI in Crime Detection
9.4 Methodology
9.5 Results and Discussion
9.5.1 Pose Detection Output
9.5.2 Punch or Stab Detection
9.5.3 Kick Detection
9.5.4 Punch Detected in Full Scale Code
9.5.5 Kick Detected in Full Scale Code
9.5.6 Fall Detected in Full Scale Code
9.5.7 Crime Detected Successfully
9.5.8 Methods for Detection Analysis
9.6 Conclusion
References
10. Zero-Knowledge Proofs for Model Integrity and Privacy
A. Kishore Kumar, T. Nivethitha, P.K. Poonguzhali and D. Saranyanandhini
10.1 Introduction
10.1.1 Motivation for Privacy and Integrity in Decentralized Learning
10.1.2 Role of Cryptography in Trustworthy AI
10.1.3 Overview of Zero-Knowledge Proofs (ZKPs) and Their Relevance
10.1.4 Fundamentals of Zero-Knowledge Proofs
10.1.5 Core Properties of ZKPs
10.1.6 Relevance of ZKPs
10.2 Decentralized Collaborative Learning: Security and Privacy Challenges
10.2.1 Threats to Model Integrity in Federated and Distributed Learning
10.2.2 Data Leakage and Model Inversion Attacks
10.2.2.1 Data Leakage
10.2.2.2 Model Inversion Attacks
10.2.2.3 Conclusions and Reductions
10.3 Introduction of ZKPs to Frameworks of Collaborative Learning
10.3.1 ZKP-Based Validation of Local Model Updates
10.3.2 Verifiable Aggregation in Federated Learning
10.3.3 Protocols for Proof of Training Compliance without Revealing Data
10.4 ZKPs for Model Provenance and Integrity Verification
10.4.1 Ensuring Tamper-Proof Model Histories
10.4.2 Transparent and Trustworthy Contributions in Collaborative Model Training
10.4.3 Use of Commitment Schemes and Cryptographic Hashes
10.4.3.1 Commitment Schemes
10.4.3.2 Cryptographic Hashes
10.4.3.3 Combined Use
10.5 Protection and Implementation Frameworks and Tools
10.5.1 ZKP Libraries and Platforms (e.g., ZoKrates, SnarkJS, Halo2)
10.5.1.1 ZoKrates
10.5.1.2 SnarkJS
10.5.1.3 Halo2
10.5.2 Integration with Federated Learning Systems (e.g., TensorFlow Federated, Pysyft)
10.5.2.1 TensorFlow Federated
10.5.2.2 PySyft
10.5.2.3 Benefits of Integration
10.5.2.4 Considerations of Practical Implementation
10.6 Use Cases and Case Studies
10.6.1 Inter-Hospital Healthcare Data Sharing
10.6.2 Privacy-Constrained Financial Fraud Detection
10.7 Conclusion and Future Directions
References
11. Edge and Fog Computing for Distributed Intelligence
Vadym Slyusar
11.1 Introduction
11.2 Differences Between Fog Computing and the Distribution of a Multi-Agent System Across Multiple Edge Devices
11.3 Combined Architecture Integrating Fog Computing and Multi-Agent Distribution at the Edge
11.4 Cognitive Decentralized Systems
11.5 Concept of Loitering Models
11.6 Migration Protocol with Decision-Making Metrics
11.7 Splitting of Models
11.8 Swarms of Loitering Models
11.9 Architecture with Distributed Embedding
11.10 The Concept of the Embedding Swarm
11.11 Hardware Aspects of the Edge Level
11.12 Conclusion
References
12. Incentive Models and Token Economics in Learning Networks
Raj Kishor Verma, Atul Kumar Rai, Kumar Dilip and Shivani Sharma
12.1 Introduction
12.1.1 Incentive Models in Learning Networks
12.1.2 Token Economics in Learning Networks
12.1.3 Emerging Challenges and Opportunities
12.2 Background
12.2.1 Early Developments: Social Incentives and Peer-to-Peer Networks
12.2.2 The Coming of Distributed Computing and Game
12.2.3 Decentralization Federated Learning and the New Distributed Paradigm
12.2.4 Token Economics
12.2.5 Game-Theoretic and AI-Enabled Incentive Models
12.2.6 Reputation- or Service-Based (and Hybrid) Incentives
12.2.7 Critical Issues: Fairness, Security and Robustness
12.3 Smart Contract Mechanisms
12.3.1 The Major Execution Differences That Matter between Smart Contract Platforms Generally Fall into the Following Categories
12.3.1.1 Consensus Mechanism and Transaction Finality
12.3.1.2 Throughput and Scalability
12.3.1.3 Programming and Virtual System Languages
12.3.1.4 Execution Costs
12.3.1.5 Security and Stability
12.3.1.6 Support for Ecosystem and Tools
12.3.2 Tokenomics
12.3.3 Alignment-Based Rewards
12.3.4 Federated Learning
12.3.5 Token Economic Frameworks
12.3.6 Decentralized Governance
12.3.7 Persistent Incentivization
12.4 Literature Review
12.5 Proposed Methodology
12.5.1 Algorithm Incentive Mechanism Learning Networks
12.5.2 Result Analysis
12.6 Conclusion and Future Scope
12.7 Challenges
References
13. Healthcare Applications of Blockchain–AI Collaboration
Rishabh Kamal and Prashant Upadhyay
13.1 Introduction
13.1.1 Synergy between Blockchain and AI in Healthcare
13.1.2 Design of a Blockchain and AI Cooperation in Healthcare
13.1.3 Blockchain for Secure and Interoperable Data
13.1.4 AI-Enhanced Clinical Decision Support
13.1.5 Federated Learning for Privacy-Preserving Analytics
13.1.6 Smart Contracts for Automated Healthcare Operations
13.1.7 Blockchain-AI Synergy for Drug Discovery and Clinical Trials
13.2 Literature Review
13.3 Fundamentals of Blockchain and Artificial Intelligence
13.3.1 Blockchain Technology in Healthcare
13.3.2 Artificial Intelligence in Healthcare
13.3.3 Smart Contracts in Healthcare
13.3.4 Machine Learning and Deep Learning in Clinical Applications
13.4 Applications of Blockchain and Artificial Intelligence in Healthcare
13.4.1 Secure Medical Data Management
13.4.2 Clinical Trials and Research Integrity
13.4.3 Healthcare Interoperability
13.4.4 Precision Medicine and Personalized Care
13.4.5 Supply Chain and Drug Authentication
13.4.6 Fraud Detection and Billing
13.4.7 Remote Patient Monitoring
13.4.8 Telemedicine and Virtual Health
13.5 Challenges in Integrating Blockchain and AI in Healthcare
13.5.1 Scalability and Performance
13.5.2 Data Quality and Bias
13.5.3 Privacy and Security Concerns
13.5.4 Ethical and Regulatory Issues
13.5.5 Interoperability Challenges
13.5.6 Cost and Resource Constraints
13.6 Future Directions in Healthcare Technology
13.6.1 Federated Learning and Decentralized AI
13.6.2 Blockchain-Oriented Internet of Medical Things
13.6.3 Patient-Centered Healthcare Systems
13.6.4 Integration with Emerging Technologies
13.7 Conclusion
References
14. Financial Sector Use Cases—Privacy-Preserving Fraud Detection
Ayush Tripathi, Prashant Upadhyay, Rupa Rani and Chadi Altrjman
14.1 Introduction
14.2 Traditional vs. Decentralized Fraud Detection Systems
14.3 Federated Learning for Collaborative Fraud Detection
14.4 Privacy-Hyphenated Systems of Financial Fraud Detection
14.5 Blockchain and Smart Contracts for Trustworthy Fraud Intelligence
14.6 Regulatory and Ethical Aspects of Privacy
14.7 Future Scope
14.8 Conclusion
References
15. Smart Cities through IoT, Blockchain, and AI Collaboration
Vishal Jain, Sachin Jain and K. Ramkumar
15.1 Introduction
15.1.1 The Genesis of Smart Cities
15.1.2 The IoT Foundation: The City’s Digital Nervous System
15.1.3 The Emerging Challenges: A Crisis of Centralization and Trust
15.1.4 A New Paradigm of Decentralized Intelligence
15.1.5 Chapter Outline
15.2 Literature Review
15.2.1 Smart Cities and the IoT
15.2.2 Blockchain in Urban Governance and Security
15.2.3 Artificial Intelligence in Smart City Operations
15.2.4 The Nascent Convergence: Combining Blockchain and AI
15.3 The Collaborative Framework: Integrating IoT, Blockchain, and AI
15.3.1 Architectural Layers
15.3.2 The Synergistic Loop: How the Components Collaborate
15.4 Mathematical and Algorithmic Foundations
15.4.1 Blockchain Consensus for IoT: Proof of Verified Authority
15.4.2 AI-Driven Predictive Analytics: LSTM for Traffic Flow
15.4.3 Smart Contract Logic with AI Triggers
15.4.4 Security and Privacy Quantification: A Conceptual Trust Index
15.5 Graphical Representations of the Ecosystem
15.6 Case Studies: Practical Applications and Implementations
15.6.1 Case Study 1: Smart Energy Grid Management
15.6.2 Case Study 2: Autonomous Traffic and Supply Chain Management
15.7 Challenges and Future Research Directions
15.8 Conclusion
References
16. Legal and Regulatory Challenges of Cross-Border AI Collaboration
Sachin Jain, Vishal Jain, Danish Ather, Golnoosh Manteghi and Abu Bakar Abdul Hamid
16.1 Introduction
16.1.1 The Imperative of Global AI Collaboration
16.1.2 The Great Friction: Technology Unites, Law Divides
16.1.3 Core Domains of Legal Conflict
16.1.4 Chapter Outline
16.2 Literature Review: Mapping the Legal Minefield
16.2.1 Data Governance and the “Splinternet”
16.2.2 Intellectual Property: The Enigma of Ownership
16.2.3 Liability and Accountability: The Distributed Responsibility Problem
16.2.4 Summary of Key Literature
16.3 Conceptual Models: Quantifying and Visualizing Legal Complexity
16.3.1 The Regulatory Friction Index
16.3.2 The Trust-Verification Matrix for Data Sharing
16.4 Descriptive Visualizations of the Legal Ecosystem
16.5 Case Studies: The Law in Action
16.5.1 Case Study 1: The Global Pandemic Prediction Initiative (Scientific Collaboration)
16.5.2 Case Study 2: “AutonoDrive”—An EU–US Autonomous Vehicle Joint Venture (Commercial Collaboration)
16.5.3 Case Study 3: The “AquaSecure” Critical Infrastructure AI (Geopolitical Collaboration)
16.6 Overarching Challenges and Future Research Directions
16.6.1 The Pacing Problem: Law Lagging Behind Technology
16.6.2 The “Black Box” Dilemma: Reconciling Opacity with Accountability
16.6.3 Bridging the Divide: The Role of Technology and Diplomacy
16.7 Conclusion
References
17. Artificial Intelligence’s Ethical Consequences: Difficulties, Hazards, and Regulatory Viewpoints
Anita Pati Mishra, Mani Dublish and Shailender Kumar Vats
17.1 Introduction
17.2 Mapping the Research Environment
17.3 Digital Transformation, the Dangers of AI, and Ethical Concerns in Research Settings
17.3.1 Hypothesis H1
17.4 Research Theories and Conceptual Framework
17.4.1 Hypothesis H1
17.4.2 Hypothesis H2
17.4.3 Hypothesis H3
17.5 Privacy, Reliability, and Cognitive Preparedness
17.6 Results
17.7 Analysis and Discussion
17.7.1 Consistent Perceptions of AI Pitfalls
17.7.2 Findings That Do Not Match
17.8 Conclusions
17.8.1 Theoretic Inferences
17.8.2 Restrictions and Additional Research
References
18. Technical Challenges and System Limitations
Sachin Jain, Vishal Jain, Danish Ather, Golnoosh Manteghi and Abu Bakar Abdul Hamid
18.1 Introduction
18.1.1 The Centralization Conundrum in the Age of AI
18.1.2 The Promise of Decentralized Collaborative Learning
18.1.3 The Hidden Complexities: Not a Privacy Panacea
18.1.4 Chapter Outline
18.2 Literature Review: A Landscape of Distributed Challenges
18.2.1 The Challenge of Statistical Heterogeneity (Non-IID Data)
18.2.2 The Communication Bottleneck
18.2.3 Security and Privacy Vulnerabilities
18.2.4 Summary of Key Literature
18.3 Conceptual Models and Mathematical Formulations
18.3.1 The Federated Averaging (FedAvg) Algorithm
18.3.2 Quantifying Statistical Heterogeneity
18.3.3 Modelling a Model Poisoning Attack
18.3.4 The Privacy-Utility Trade-Off with DP
18.4 Descriptive Visualizations of System Limitations
18.5 Case Studies: Challenges in Real-World Application
18.5.1 Case Study 1: Collaborative Cancer Detection in a Hospital Consortium
18.5.2 Case Study 2: Next-Word Prediction for a Mobile Keyboard Application
18.5.3 Case Study 3: Predictive Maintenance in a B2B Industrial IoT Consortium
18.6 System-Level Limitations and Future Research Directions
18.6.1 The Centralized Orchestrator
18.6.2 The Right to be Forgotten and Machine “Unlearning”
18.6.3 Systems Heterogeneity
18.6.4 At the Cutting Edge: Integrating DCL with Other Technologies to Strengthen Privacy
18.7 Conclusion
References
19. Trust by Design: AI’s Evolution in Secure and Transparent Data Systems
Nandini Srivastava and Anuradha M. Dhumale
19.1 Introduction
19.2 Evolution of AI in Data Security and Transparency
19.2.1 Artificial Intelligence
19.2.2 Early Artificial Intelligence Systems
19.2.3 Current Trends of AI
19.2.3.1 Blockchain Technology
19.2.3.2 Federated Learning
19.3 Machine Learning
19.3.1 Introduction
19.3.2 Types of Machine Learning
19.3.2.1 Supervised Learning
19.3.2.2 Unsupervised Learning
19.3.2.3 Semi-Supervised Learning
19.3.2.4 Reinforcement Learning
19.3.3 Classification
19.3.3.1 Lazy Learning
19.3.3.2 Eager Learning
19.4 Trust in AI
19.4.1 Trust
19.4.2 Bias and Discrimination
19.4.3 Trust by Design Principle
19.4.3.1 Transparent and Explainability
19.4.3.2 Accountability
19.4.3.3 Privacy Preservation
19.4.3.4 Security by Default
19.4.3.5 Ethical AI Governance
19.5 Comparison Table and Use Cases
19.5.1 AI in Healthcare
19.5.2 AI in Education
19.5.3 AI in Finance
19.5.4 AI in Smart Cities
19.5.5 AI Tools
19.6 Conclusion
References
20. Decentralized Intelligence: Redefining Learning through Secure AI and EoT Integration
Ravipalli Sri Santhi Nehru
20.1 Introduction
20.1.1 The Changing Landscape of Education
20.1.2 Motivation for Decentralization and Intelligence Integration
20.1.3 Objectives and Scope of the Chapter
20.2 Conceptual Foundations
20.2.1 Decentralized Intelligence: What is It?
20.2.2 CLF Innovative Environments and EoT
20.2.3 Role of Secure AI in Modern Education
20.2.4 Summary of Privacy-Preserving Models and Trust-Based Learning Models
20.3 Architecture of the Decentralized Learning Ecosystem
20.3.1 Integration of Blockchain and Distributed Ledger Technologies
20.3.2 AI-Driven Personalization and Adaptability
20.3.3 EoT Infrastructure: The Sensors, Edge Devices, and Data Flow
20.3.4 Real-Time Data Processing Using Edge Computing and Associated Techniques
20.4 Foundational Technologies for Privacy-Preserving, Distributed Education
20.4.1 Blockchain for Learners’ Identity and Attainment Records
20.4.2 Data Sovereignty and Federated Learning
20.4.3 Thrustless Security Protocols and Homomorphic Encryption
20.4.4 Smart Contracts and Educational Governance
20.5 Applications and Use Cases
20.5.1 Smart Classrooms and Interactive Learning Environments
20.5.2 Blockchain-Based Credentialing and Micro-Certification
20.5.3 Context-Aware Learning Analytics and Feedback Systems
20.5.4 Wearables and Mobile Devices in Adaptive Learning
20.6 Issues Related to the Technology and Their Solutions
20.6.1 Keeping Things the Same and in Synchronization within Distributed Frameworks
20.6.2 Assuring the Data is Correct While Also Managing the Available Bandwidth
20.6.3 Techniques on EoT Data Stream Compression
20.6.4 Being Able to Manage Things in Learning Environments That are Constantly Changing
20.7 Social, Policy, and Ethical Aspects
20.7.1 Algorithmic Equity and Reducing Bias
20.7.2 The Digital Divide and Equitable Access
20.7.3 Learner Privacy and Data Ethics
20.7.4 Decentralized Education Policy Frameworks
20.8 New Technologies and What to Expect in the Future
20.8.1 Advanced Security Models and Quantum Computing
20.8.2 Learning Ecosystems That Work Across Chains
20.8.3 Interoperability and Standardization in Decentralized Education
20.8.4 A Vision for Systems that are Totally Adaptive Learner-Centric
20.9 Conclusion
References
21. Fortifying Financial Systems: Privacy-Centric Collaborative Detection with Transparent Accountability
Jarnail Singh and Shelley Khosla
21.1 Introduction
21.2 Literature Review
21.2.1 Federated Learning in Anomaly Detection
21.2.2 Blockchain for Security and Transparency
21.2.3 Privacy-Preserving Techniques
21.2.4 Open Challenges
21.3 Methodology
21.3.1 System Architecture
21.3.2 Local Autoencoder Model
21.3.3 Federated Learning Protocol
21.3.4 Privacy and Security
21.3.5 Handling Class Imbalance and Heterogeneity
21.4 Experimental Setup
21.4.1 Evaluation Metrics
21.4.2 Experimental Environment
21.5 Results
21.6 Discussion
21.7 Challenges and Future Directions
21.8 Conclusion
Abbreviations
References
Index

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Decentralized Collaborative Learning for Data Privacy and Security