Federated Learning in Finance
Unlocking Privacy-Preserving and Cyber Resilience using AI
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 13 May 2026
- ISBN 9781041115106
- Binding Hardback
- No. of pages342 pages
- Size 254x178 mm
- Language English
- Illustrations 46 Illustrations, black & white; 4 Halftones, black & white; 42 Line drawings, black & white; 32 Tables, black & white 700
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Short description:
Federated Intelligence: Unlocking Privacy-Preserving and Cyber Resilience using AI in the Finance Industry" is an edited volume designed to explores how Federated Intelligence can help the finance industry defend against cyber threats, detect fraud, and comply with regulations.
MoreLong description:
Federated Intelligence: Unlocking Privacy-Preserving and Cyber Resilience using AI in the Finance Industry" is an edited volume designed to explores how Federated Intelligence can help the finance industry defend against cyber threats, detect fraud, and comply with regulations, all while keeping sensitive financial data secure and distributed.
This book provides a comprehensive roadmap for integrating Federated Learning (FL) and AI-driven cyber security into financial ecosystems. Unlike conventional AI systems that require data centralization, Federated Intelligence enables financial institutions to collaborate securely, train powerful AI models, and combat cyber threats.
MoreTable of Contents:
Chapter 1: Regulatory Challenges and Compliance in Federated Learning for Financial Applications. Chapter 2: The Mechanism of Federated Learning. Chapter 3: Federated Learning for Fraud Detection and Risk Mitigation in Financial Systems. Chapter 4: Cybersecurity Vulnerabilities in Federated Learning. Chapter 5: Zero Trust Principles in AI-Driven Architectures: Security by Design. Chapter 6: Data Poisoning and Adversarial Attacks in Federated Learning. Chapter 7: Securing Digital Payments and Transactions Using Federated Learning. Chapter 8: Blockchain and Federated Learning. Chapter 9: Cyber Resilience Through Adaptive Federated Learning. Chapter 10: Quantum Threats and Federated Learning. Chapter 11: Next-Gen and Autonomous Federated Systems. Chapter 12: A Roadmap for Federated Learning Adoption. Chapter 13: Cyber Resilience in Sports Organisations: Federal Learning for Financial, Fan, and Athlete Data Security.
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