Note: Intro -- Preface -- Contents -- Nomenclature -- Greek Symbols -- Subscripts -- Chapter 1: Introduction -- Chapter 2: Prior Research -- 2.1 Frost Formation -- Frost Nucleation -- Experiments and Correlations -- Analytical Models -- Effects of Surface Wettability -- 2.2 The Defrost Process -- Effect of Surface Wettability -- Frost Release and Condensate Retention -- Analytical Models -- Ice Adhesion -- 2.3 Summary -- Chapter 3: A Model of the Defrost Process -- 3.1 General Description of the Melting Model -- 3.2 Meltwater Absorption -- 3.3 Meltwater Accumulation -- 3.4 Meltwater Draining -- 3.5 Defrost Time and Efficiency -- 3.6 Slumping Criteria -- Force Balance in the Absorption Process -- The Force Balance During Meltwater Accumulation -- The Force Balance During Meltwater Draining -- Chapter 4: Solution Methods -- 4.1 Absorption -- 4.2 Accumulation -- 4.3 Draining -- 4.4 Slumping Criteria -- Chapter 5: Experimental Design -- 5.1 Apparatus -- 5.2 Design Analysis -- Chamber Energy Balance -- Heat Transfer Coefficient on the Test Surface and Frost -- Heat Flux at the Test Surface -- Heat Sink -- Capacity of the Thermoelectric Module -- 5.3 Data Acquisition and Uncertainty Estimates -- Uncertainty Estimates -- 5.4 Surface Preparation and Characterization -- Chapter 6: Results -- 6.1 Analytical and Numerical Results -- Slumping Criterion -- 6.2 Experimental Results -- 6.3 Comparison of Predictions and Measurements -- Chapter 7: Conclusion -- 7.1 Future Research Pathways -- Instruments and Precision -- Slumping Image -- References.

Note: Intro -- Supervisor's Foreword -- Abstract -- Parts of this thesis have been published in the following journal articles: -- Acknowledgements -- Contents -- List of Figures -- 1 Introduction -- 1.1 Specific Aims -- 1.2 Nanotheranostics -- 1.3 Surface Enhanced Raman Spectroscopy (SERS) -- 1.4 Two-Photon Photoluminescence (TPL) -- 1.5 X-ray Computed Tomography (CT) -- 1.6 Magnetic Resonance Imaging (MRI) -- 1.7 Positron Emission Tomography (PET) -- 1.8 Photoimmunotherapy -- 1.9 Summary and Study Outline -- References -- 2 Multifunctional GNS Nanoprobe Development and Characterization -- 2.1 Multifunctional GNS Nanoprobe Development -- 2.2 Multifunctional GNS Characterization -- 2.3 pH Sensing with GNS Nanoprobe Using SERS -- 2.4 GNS Photothermal Conversion Evaluation -- 2.5 Summary -- References -- 3 In Vivo Evaluation of GNS Nanoprobe -- 3.1 Background -- 3.2 PET Imaging with Radiolabeled GNS Nanoprobe -- 3.3 CT Imaging with GNS Nanoprobe -- 3.4 In Vivo SERS Detection -- 3.5 TPL for GNS Tracking at Subcellular Level -- 3.6 GNS Biodistribution Study with Radiolabeling -- 3.7 In Vivo Photothermal Therapy -- 3.8 Summary -- References -- 4 GNS Toxicity Investigation -- 4.1 Background -- 4.2 In Vitro Toxicity Study -- 4.3 In Vivo Toxicity Study -- 4.4 Summary -- References -- 5 Sensitive Brain Tumor Detection Using GNS Nanoprobe -- 5.1 Background -- 5.2 Serial PET Imaging for GNS Tumor Uptake Study -- 5.3 Comparison of GNS and FDG Radiotracer for Brain Tumor Detection -- 5.4 Tumor Detection Limit Using GNS Nanoprobe -- 5.5 Study of GNS Uptake in Brain Tumor -- 5.6 Summary -- References -- 6 Photoimmunotherapy for Cancer Metastasis Treatment -- 6.1 Background -- 6.2 Window Chamber Imaging and In Vivo Photothermal Study -- 6.3 Long Term Survival and Rechallenge Study for Photoimmunotherapy -- 6.4 Summary -- References -- 7 Conclusion and Future Outlook. , 7.1 Conclusion -- 7.2 Future Outlook -- About the Author -- Provisional Patent.

Note: Intro -- Foreword -- Preface -- Contents -- Contributors -- Acronyms -- 1 A Joint Introduction to Natural Language Processing and to Deep Learning -- 1.1 Natural Language Processing: The Basics -- 1.2 The First Wave: Rationalism -- 1.3 The Second Wave: Empiricism -- 1.4 The Third Wave: Deep Learning -- 1.5 Transitions from Now to the Future -- 1.5.1 From Empiricism to Deep Learning: A Revolution -- 1.5.2 Limitations of Current Deep Learning Technology -- 1.6 Future Directions of NLP -- 1.6.1 Neural-Symbolic Integration -- 1.6.2 Structure, Memory, and Knowledge -- 1.6.3 Unsupervised and Generative Deep Learning -- 1.6.4 Multimodal and Multitask Deep Learning -- 1.6.5 Meta-learning -- 1.7 Summary -- References -- 2 Deep Learning in Conversational Language Understanding -- 2.1 Introduction -- 2.2 A Historical Perspective -- 2.3 Major Language Understanding Tasks -- 2.3.1 Domain Detection and Intent Determination -- 2.3.2 Slot Filling -- 2.4 Elevating State of the Art: From Statistical Modeling to Deep Learning -- 2.4.1 Domain Detection and Intent Determination -- 2.4.2 Slot Filling -- 2.4.3 Joint Multitask Multi-domain Modeling -- 2.4.4 Understanding in Context -- 2.5 Summary -- References -- 3 Deep Learning in Spoken and Text-Based Dialog Systems -- 3.1 Introduction -- 3.2 Learning Methodology for Components of a Dialog System -- 3.2.1 Discriminative Methods -- 3.2.2 Generative Methods -- 3.2.3 Decision-Making -- 3.3 Goal-Oriented Neural Dialog Systems -- 3.3.1 Neural Language Understanding -- 3.3.2 Dialog State Tracker -- 3.3.3 Deep Dialog Manager -- 3.4 Model-Based User Simulators -- 3.5 Natural Language Generation -- 3.6 End-to-End Deep Learning Approaches to Building Dialog Systems -- 3.7 Deep Learning for Open Dialog Systems -- 3.8 Datasets for Dialog Modeling -- 3.8.1 The Carnegie Mellon Communicator Corpus. , 3.8.2 ATIS-Air Travel Information System Pilot Corpus -- 3.8.3 Dialog State Tracking Challenge Dataset -- 3.8.4 Maluuba Frames Dataset -- 3.8.5 Facebook's Dialog Datasets -- 3.8.6 Ubuntu Dialog Corpus -- 3.9 Open Source Dialog Software -- 3.10 Dialog System Evaluation -- 3.11 Summary -- References -- 4 Deep Learning in Lexical Analysis and Parsing -- 4.1 Background -- 4.2 Typical Lexical Analysis and Parsing Tasks -- 4.2.1 Word Segmentation -- 4.2.2 POS Tagging -- 4.2.3 Syntactic Parsing -- 4.2.4 Structured Predication -- 4.3 Structured Prediction Methods -- 4.3.1 Graph-Based Methods -- 4.3.2 Transition-Based Methods -- 4.4 Neural Graph-Based Methods -- 4.4.1 Neural Conditional Random Fields -- 4.4.2 Neural Graph-Based Dependency Parsing -- 4.5 Neural Transition-Based Methods -- 4.5.1 Greedy Shift-Reduce Dependency Parsing -- 4.5.2 Greedy Sequence Labeling -- 4.5.3 Globally Optimized Models -- 4.6 Summary -- References -- 5 Deep Learning in Knowledge Graph -- 5.1 Introduction -- 5.1.1 Basic Concepts -- 5.1.2 Typical Knowledge Graphs -- 5.2 Knowledge Representation Learning -- 5.3 Neural Relation Extraction -- 5.3.1 Sentence-Level NRE -- 5.3.2 Document-Level NRE -- 5.4 Bridging Knowledge with Text: Entity Linking -- 5.4.1 The Entity Linking Framework -- 5.4.2 Deep Learning for Entity Linking -- 5.5 Summary -- References -- 6 Deep Learning in Machine Translation -- 6.1 Introduction -- 6.2 Statistical Machine Translation and Its Challenges -- 6.2.1 Basics -- 6.2.2 Challenges in Statistical Machine Translation -- 6.3 Component-Wise Deep Learning for Machine Translation -- 6.3.1 Deep Learning for Word Alignment -- 6.3.2 Deep Learning for Translation Rule Probability Estimation -- 6.3.3 Deep Learning for Reordering Phrases -- 6.3.4 Deep Learning for Language Modeling -- 6.3.5 Deep Learning for Feature Combination. , 6.4 End-to-End Deep Learning for Machine Translation -- 6.4.1 The Encoder-Decoder Framework -- 6.4.2 Neural Attention in Machine Translation -- 6.4.3 Addressing Technical Challenges of Large Vocabulary -- 6.4.4 End-to-End Training to Optimize Evaluation Metric Directly -- 6.4.5 Incorporating Prior Knowledge -- 6.4.6 Low-Resource Language Translation -- 6.4.7 Network Structures in Neural Machine Translation -- 6.4.8 Combination of SMT and NMT -- 6.5 Summary -- References -- 7 Deep Learning in Question Answering -- 7.1 Introduction -- 7.2 Deep Learning in Question Answering over Knowledge Base -- 7.2.1 The Information Extraction Style -- 7.2.2 The Semantic Parsing Style -- 7.2.3 The Information Extraction Style Versus the Semantic Parsing Style -- 7.2.4 Datasets -- 7.2.5 Challenges -- 7.3 Deep Learning in Machine Comprehension -- 7.3.1 Task Description -- 7.3.2 Feature Engineering-Based Methods in Machine Comprehension -- 7.3.3 Deep Learning Methods in Machine Comprehension -- 7.4 Summary -- References -- 8 Deep Learning in Sentiment Analysis -- 8.1 Introduction -- 8.2 Sentiment-Specific Word Embedding -- 8.3 Sentence-Level Sentiment Classification -- 8.3.1 Convolutional Neural Networks -- 8.3.2 Recurrent Neural Networks -- 8.3.3 Recursive Neural Networks -- 8.3.4 Integration of External Resources -- 8.4 Document-Level Sentiment Classification -- 8.5 Fine-Grained Sentiment Analysis -- 8.5.1 Opinion Mining -- 8.5.2 Targeted Sentiment Analysis -- 8.5.3 Aspect-Level Sentiment Analysis -- 8.5.4 Stance Detection -- 8.5.5 Sarcasm Recognition -- 8.6 Summary -- References -- 9 Deep Learning in Social Computing -- 9.1 Introduction to Social Computing -- 9.2 Modeling User-Generated Content with Deep Learning -- 9.2.1 Traditional Semantic Representation Approaches -- 9.2.2 Semantic Representation with Shallow Embedding. , 9.2.3 Semantic Representation with Deep Neural Networks -- 9.2.4 Enhancing Semantic Representation with Attention Mechanism -- 9.3 Modeling Social Connections with Deep Learning -- 9.3.1 Social Connections on Social Media -- 9.3.2 A Network Representation Learning Approach to Modeling Social Connections -- 9.3.3 Shallow Embedding Based Models -- 9.3.4 Deep Neural Network Based Models -- 9.3.5 Applications of Network Embedding -- 9.4 Recommendation with Deep Learning -- 9.4.1 Recommendation on Social Media -- 9.4.2 Traditional Recommendation Algorithms -- 9.4.3 Shallow Embedding Based Models -- 9.4.4 Deep Neural Network Based Models -- 9.5 Summary -- References -- 10 Deep Learning in Natural Language Generation from Images -- 10.1 Introduction -- 10.2 Background -- 10.3 Deep Learning Frameworks to Generate Natural Language from an Image -- 10.3.1 The End-to-End Framework -- 10.3.2 The compositional framework -- 10.3.3 Other Frameworks -- 10.4 Evaluation Metrics and Benchmarks -- 10.5 Industrial Deployment of Image Captioning -- 10.6 Examples: Natural Language Descriptions of Images -- 10.7 Recent Research on Generating Stylistic Natural Language from Images -- 10.8 Summary -- References -- 11 Epilogue: Frontiers of NLP in the Deep Learning Era -- 11.1 Introduction -- 11.2 Two New Perspectives -- 11.2.1 The Task-Centric Perspective -- 11.2.2 The Representation-Centric Perspective -- 11.3 Major Recent Advances in Deep Learning for NLP and Research Frontiers -- 11.3.1 Compositionality for Generalization -- 11.3.2 Unsupervised Learning for NLP -- 11.3.3 Reinforcement Learning for NLP -- 11.3.4 Meta-Learning for NLP -- 11.3.5 Interpretability: Weak-Sense and Strong-Sense -- 11.4 Summary -- References -- Appendix Glossary.

Note: Intro -- Introduction: Energy Efficiency Financing and Market-Based Instruments -- Acknowledgments -- Contents -- About the Editors -- Part I Driving Factors of Energy Efficiency Financing -- 1 Understanding Cross-Economy Dynamics of Energy Efficiency: Driving Factors and Stylized Patterns -- Abstract -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Energy Efficiency Development Between 2000 and 2017 -- 1.3.1 Improved Energy Efficiency -- 1.3.2 Driving Factors of Energy Efficiency -- 1.4 Distribution Dynamics of Energy Efficiency -- 1.5 Distribution Approach -- 1.6 Distribution of Energy Efficiency in the Steady State -- 1.7 Conclusions and Policy Implications -- Appendix A Economies and Sectors -- Appendix B -- References -- 2 Off-Balance Sheet Equity: The Engine for Energy Efficiency Capital Mobilization -- Abstract -- 2.1 Energy Efficiency Outlook -- 2.2 Business-as-Usual Financing -- 2.3 Energy Service Companies (ESCOs) -- 2.4 ESCO Markets in Asia -- 2.5 Market Enablers -- 2.6 Case Studies -- 2.7 Conclusion and Recommendations -- References -- 3 Review of Voluntary Agreements on Energy Efficiency: Promoting Energy Efficiency Financing in ASEAN Countries -- Abstract -- 3.1 Introduction -- 3.2 Voluntary Agreements Mechanism -- 3.2.1 Implementation Process -- 3.2.2 Advantages and Disadvantages of Voluntary Agreements -- 3.2.3 Company-Level Motivation to Participate in Voluntary Agreements -- 3.3 Voluntary Agreements and Energy Efficiency in ASEAN Countries -- 3.3.1 Energy Efficiency of ASEAN Countries -- 3.3.2 Voluntary Agreements and Other Market-Based Instruments to Improve Industrial Energy Efficiency of ASEAN Countries -- 3.3.3 Need for Voluntary Agreements to Overcome Challenges for Energy Efficiency Financing -- 3.4 Lessons from Successful Voluntary Agreements -- 3.4.1 Selected Voluntary Agreements -- 3.4.2 Comparison of Design Elements. , 3.4.3 Lessons for ASEAN Countries -- 3.5 Conclusion and Policy Implications -- References -- 4 R& -- D Investments in Energy Efficiency, Economic Impact, and Emissions Abatement -- Abstract -- 4.1 Introduction -- 4.2 R& -- D Investments in Energy Efficiency -- 4.2.1 Description of Energy R& -- D Investments -- 4.2.2 Energy Efficiency R& -- D Projects from the Supply Side -- 4.3 Literature Review -- 4.3.1 Financing Energy Projects -- 4.3.2 Induced Technological Change and Energy R& -- D Investment -- 4.4 Model -- 4.4.1 Utility -- 4.4.2 Production Sector -- 4.4.3 Modeling Energy Services and R& -- D Investments in Energy Efficiency -- 4.4.4 Emissions and Regulation -- 4.5 Policy Regimes -- 4.6 Results -- 4.6.1 R& -- D Investments -- 4.6.2 Economic Impacts -- 4.6.3 Energy Substitution, Carbon Emissions, and Climate Change -- 4.7 Conclusion and Recommendations -- References -- Part II Regional Studies -- 5 Nexus of Energy Efficiency and Energy Access in ASEAN: Trends and Financing Schemes -- Abstract -- 5.1 Introduction -- 5.1.1 Background of the Study -- 5.2 Literature Review -- 5.2.1 Nexus of Energy Access and Energy Efficiency in the Context of ASEAN -- 5.2.2 Growing Challenges of Future Energy Demand in ASEAN -- 5.3 Energy Efficiency in ASEAN -- 5.3.1 Trends in ASEAN -- 5.3.2 Energy Targets -- 5.3.3 Investment in Energy Efficiency -- 5.4 Financing Schemes for Energy Efficiency -- 5.4.1 Energy Efficiency and Conservation Institutional and Policy Framework at the ASEAN Level -- 5.4.2 Financing Schemes and Instruments for Energy Efficiency and Conservation Projects Used in ASEAN -- 5.4.3 Types of Energy Efficiency and Conservation Financing Required for ASEAN -- 5.4.4 Replicability of Innovative Financial Schemes -- 5.4.5 Barriers to Improving Energy Efficiency -- 5.5 Policy Recommendation and Conclusion -- References. , 6 The Role of Fiscal Incentives and Market-Based Incentives in Promoting Energy Efficiency in the Industrial Sector: Case Studies from Asia -- Abstract -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Review of Policies Improving Energy Efficiency -- 6.3.1 Policies Incentivizing Energy Efficiency -- 6.3.2 Market-Based Instruments -- 6.4 Energy Efficiency in Selected Asian Countries -- 6.4.1 People's Republic of China -- 6.4.2 India -- 6.4.3 Indonesia -- 6.4.4 Japan -- 6.5 Conclusion and Policy Recommendations -- References -- 7 Promoting Energy Efficiency Through Foreign Direct Investments: Evidence from South Asian Countries -- Abstract -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Methodology and Data -- 7.3.1 Empirical Model -- 7.3.2 Panel Co-integration Test -- 7.3.3 Panel FMOLS and DOLS Estimation for Long Run -- 7.3.4 Panel Granger Causality Test -- 7.3.5 Data -- 7.4 Empirical Results -- 7.4.1 Panel Unit Root Test -- 7.4.2 Results of Co-integration -- 7.4.3 Panel Estimation Results -- 7.4.4 Panel Granger Causality Test -- 7.5 Conclusions and Policy Recommendations -- References -- 8 The Effect of Global Value Chain Participation and Position on Energy Efficiency in Belt and Road Countries -- Abstract -- 8.1 Introduction -- 8.2 Literature Review -- 8.2.1 Energy Efficiency Financing -- 8.2.2 Global Value Chains -- 8.2.3 Belt and Road Initiative -- 8.3 Methodology -- 8.3.1 Design -- 8.3.2 Sampling Technique and the Data -- 8.3.3 Variables Used -- 8.4 Empirical Results -- 8.4.1 Level of GVC Participation Before and After the BRI -- 8.4.2 Determining the Level of GVC Position After BRI -- 8.4.3 Impact of GVC Participation Before and After the BRI on Energy Efficiency Investment -- 8.5 Conclusions and Policy Recommendations -- Acknowledgements -- Conflict of Interest -- References -- Part III Country Case Studies. , 9 District Heating Business Models and Policy Solutions: Financing Utilization of Low-Grade Industrial Excess Heat in the People's Republic of China -- Abstract -- 9.1 Introduction -- 9.2 Opportunities and Challenges in the PRC's District Heating Market -- 9.2.1 The PRC's Low-Grade Excess Heat Potential -- 9.2.2 Characteristics of District Heating Systems -- 9.2.3 Challenges in the District Heating Market -- 9.3 Business Models to Enhance Energy Efficiency of District Heating -- 9.3.1 Utility-Led with Third-Party Access Model -- 9.3.2 Heat Production Competition Model -- 9.3.3 ESCO-Integrated Business Model -- 9.4 Pricing Industrial Excess Heat -- 9.4.1 Impact of Industrial Excess Heat on District Heating Prices -- 9.5 Conclusions and Policy Recommendations -- References -- 10 Market-Led Energy Efficiency Transformation in India: A Deep Dive into the Perform, Achieve, Trade (PAT) Scheme -- Abstract -- 10.1 Background -- 10.2 Policy Landscape Governing Energy Efficiency Initiatives in India -- 10.3 Trends and Patterns of Energy Intensity in India: A Snapshot -- 10.4 Perform, Achieve, and Trade (PAT) Scheme: Brief Review of Scholarly Writings -- 10.5 Market-Led Energy Efficiency Transformation in India: The Case of the PAT Scheme -- 10.6 Financing Energy Efficiency Projects -- 10.6.1 Energy Efficiency Financial Platform -- 10.6.2 Framework for Energy Efficient Economic Development -- 10.6.3 Concessional Financing through IREDA -- 10.6.4 State Energy Conservation Funds -- 10.6.5 ESCO-Based Financing -- 10.7 Obstructions for Uptake of Energy Efficiency Interventions under the PAT Scheme -- 10.8 Conclusion -- References -- 11 Financing of Energy Efficiency in Public Goods: The Case of Street Lighting Systems in Indonesia -- Abstract -- 11.1 Introduction -- 11.2 Review of Global Instruments for Energy Efficiency -- 11.3 Policy Challenges in Indonesia. , 11.4 ESCO Case Study in Indonesia -- 11.5 Policy Recommendations -- 11.5.1 Auction Mechanism Improvements -- 11.5.2 Design a Super ESCO to Strengthen the Financial and Technical Capacity of Private ESCOs -- 11.6 Conclusion -- References -- 12 The Viability of Green Bonds as a Financing Mechanism for Energy-Efficient Green Buildings in ASEAN: Lessons from Malaysia and Singapore -- Abstract -- 12.1 Introduction -- 12.2 Outlook on Investment in Green Buildings in ASEAN -- 12.2.1 Incentives to Invest in Green Buildings -- 12.2.2 Reasons for Under-Investment in Green Buildings in ASEAN -- 12.3 Issuance of Green Bonds to Fund Energy-Efficient Buildings -- 12.3.1 Suitability of Green Bonds for Financing Energy Efficiency Projects -- 12.3.2 Landscape of Green Bonds in ASEAN -- 12.3.3 Green Bonds Issued to Fund Green Building Projects in ASEAN -- 12.3.4 Green Bonds as an Effective Instrument to Finance Green Buildings in Singapore and Malaysia -- 12.3.5 Challenges in the Widespread Adoption of Green Bonds to Finance Green Building Projects -- 12.4 Conclusion and Policy Recommendations -- 12.4.1 Enabling the Demand for Green Bonds Through Information Provision -- 12.4.2 Endorsement of Green Buildings Through Building Codes -- 12.4.3 Promoting Local Currency Bond Financing Through Domestic Institutional Investors -- Appendix -- References -- 13 Energy Efficiency Financing in Viet Nam: Current Status and Solutions Toward Market-Based Mechanism Adoption -- Abstract -- 13.1 Introduction -- 13.2 Current Status of Energy Efficiency in Viet Nam -- 13.2.1 Energy Conservation Potential in Different Sectors of the Economy -- 13.3 Financing for Energy Efficiency in Viet Nam -- 13.3.1 Energy Efficiency Investment Need -- 13.3.2 Financial Instruments to Unlock Energy Efficiency Potential in Viet Nam -- 13.4 Barriers in Accelerating Energy Efficiency Financing in Viet Nam. , 13.5 Conclusion and Policy Recommendations.

Note: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Introduction -- Motivation -- Federated Learning as a Solution -- The Definition of Federated Learning -- Categories of Federated Learning -- Current Development in Federated Learning -- Research Issues in Federated Learning -- Open-Source Projects -- Standardization Efforts -- The Federated AI Ecosystem -- Organization of this Book -- Background -- Privacy-Preserving Machine Learning -- PPML and Secure ML -- Threat and Security Models -- Privacy Threat Models -- Adversary and Security Models -- Privacy Preservation Techniques -- Secure Multi-Party Computation -- Homomorphic Encryption -- Differential Privacy -- Distributed Machine Learning -- Introduction to DML -- The Definition of DML -- DML Platforms -- Scalability-Motivated DML -- Large-Scale Machine Learning -- Scalability-Oriented DML Schemes -- Privacy-Motivated DML -- Privacy-Preserving Decision Trees -- Privacy-Preserving Techniques -- Privacy-Preserving DML Schemes -- Privacy-Preserving Gradient Descent -- Vanilla Federated Learning -- Privacy-Preserving Methods -- Summary -- Horizontal Federated Learning -- The Definition of HFL -- Architecture of HFL -- The Client-Server Architecture -- The Peer-to-Peer Architecture -- Global Model Evaluation -- The Federated Averaging Algorithm -- Federated Optimization -- The FedAvg Algorithm -- The Secured FedAvg Algorithm -- Improvement of the FedAvg Algorithm -- Communication Efficiency -- Client Selection -- Related Works -- Challenges and Outlook -- Vertical Federated Learning -- The Definition of VFL -- Architecture of VFL -- Algorithms of VFL -- Secure Federated Linear Regression -- Secure Federated Tree-Boosting -- Challenges and Outlook -- Federated Transfer Learning -- Heterogeneous Federated Learning -- Federated Transfer Learning -- The FTL Framework. , Additively Homomorphic Encryption -- The FTL Training Process -- The FTL Prediction Process -- Security Analysis -- Secret Sharing-Based FTL -- Challenges and Outlook -- Incentive Mechanism Design for Federated Learning -- Paying for Contributions -- Profit-Sharing Games -- Reverse Auctions -- A Fairness-Aware Profit Sharing Framework -- Modeling Contribution -- Modeling Cost -- Modeling Regret -- Modeling Temporal Regret -- The Policy Orchestrator -- Computing Payoff Weightage -- Discussions -- Federated Learning for Vision, Language, and Recommendation -- Federated Learning for Computer Vision -- Federated CV -- Related Works -- Challenges and Outlook -- Federated Learning for NLP -- Federated NLP -- Related Works -- Challenges and Outlook -- Federated Learning for Recommendation Systems -- Recommendation Model -- Federated Recommendation System -- Related Works -- Challenges and Outlook -- Federated Reinforcement Learning -- Introduction to Reinforcement Learning -- Policy -- Reward -- Value Function -- Model of the Environment -- RL Example -- Reinforcement Learning Algorithms -- Distributed Reinforcement Learning -- Asynchronous Distributed Reinforcement Learning -- Synchronous Distributed Reinforcement Learning -- Federated Reinforcement Learning -- Challenges and Outlook -- Selected Applications -- Finance -- Healthcare -- Education -- Urban Computing and Smart City -- Edge Computing and Internet of Things -- Blockchain -- 5G Mobile Networks -- Summary and Outlook -- Legal Development on Data Protection -- Bibliography -- Authors' Biographies.

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