Preface. Series Preface.
1. Introduction to Federated Learning in Financial Services.
2. Federated Learning Demystified: Core Concepts, Working, Benefits, and Challenges.
3. Federated Learning for Financial Data Privacy and Security in Secure AI Systems.
4. Governance and Data Management in Federated Learning.
5. Credit Risk Assessment and Scoring Models.
6. Fraud Detection and Anti-Money Laundering (AML).
7. Wealth Management and Investment Strategies.
8. Building Federated Learning Infrastructure in Finance.
9. Federated Learning for Financial Data Privacy and Security Services.
10. Cross-Border Collaboration and Data Exchange.
11. Federated Learning for ESG Disclosure Analytics: A Study on NSE Sectoral Indices.
12. Application of Machine Learning for Detecting Financial Irregularities in Local Government Revenue Collection Information System.
13. Regulatory and Ethical Considerations in Federated Finance.
14. Emerging Trends and Future Innovations.
15. Evaluation of Logistic Regression and Decision Tree Classifiers to Detect Smurf Attacks on a Network Interface.
16. Protecting the Sensitive Data: Federated Learning System Privacy Improvements. Index. About the Editors.