Federated Learning in Finance

Unlocking Privacy-Preserving and Cyber Resilience using AI
1. Auflage 2026
ISBN: 978-1-041-11510-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 328 Seiten, Format (B × H): 178 mm x 254 mm

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.

Zielgruppe

Academic, Postgraduate, and Professional Reference

Autoren/Hrsg.

Tyagi, Aditya Dayal

Sah, Swati

Sulaiman, Rejwan Bin

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Schadprogramme (Viren, Trojaner etc.)
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Kryptographie, Datenverschlüsselung
• Mathematik | Informatik EDV | Informatik Digital Lifestyle Online Banking & Finance

Inhaltsverzeichnis

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.

Über Autor(innen)

Dr. Swati Sah is currently serving as a Professor at Sharda University, India. Prior to this, she held an academic position at Amity University, Uzbekistan. In May 2018, she was appointed as Head of the Department of Computer Science at Patan College for Professional Studies (PCPS), Nepal, an institution affiliated with the University of Bedfordshire, UK. Dr. Sah holds a Master of Computer Applications (MCA) degree from Uttar Pradesh Technical University, Lucknow, India, and an M.Sc. from Birmingham City University, United Kingdom.

With over 12 years of experience in teaching and research, she has been actively engaged with various professional associations and academic bodies. Her research interests lie in the areas of Cyber Security, Artificial Intelligence (AI), and Machine Learning (ML). She has contributed to several scholarly publications and has presented her work at international conferences. Her current work focuses on leveraging AI and ML techniques to enhance cyber threat detection and prevention frameworks. She is also passionate about interdisciplinary applications of emerging technologies and continues to explore innovative solutions addressing real-world challenges in digital security and intelligent systems.

Dr. Rejwan Bin Sulaiman is currently serving as a Lecturer in Cyber Security at the University of Law, United Kingdom. He earned his Ph.D. in Artificial Intelligence and Cybersecurity from the University of Bedfordshire, where his doctoral research focused on federated learning-based approaches for secure and privacy-preserving financial AI systems. He has held academic positions at several institutions, including Northumbria University and Arden University.

Dr. Sulaiman’s research spans Cybersecurity, Artificial Intelligence, Computer Vision, and Machine Learning, with particular interest in developing decentralized AI models that enhance data security and user privacy. His work has been published in leading venues such as IEEE, Springer, and CRC Press, contributing to the advancement of secure machine learning frameworks in distributed environments.

He is a Certified Ethical Hacker (CEH) and the founder of STEMResearch.Ai, an initiative that supports and mentors early-career researchers in STEM fields. He is also a Fellow of the Higher Education Academy (FHEA) and has received multiple awards recognizing his innovative teaching practices and dedication to academic excellence

Aditya Dayal Tyagi is currently serving as an Assistant Professor at Sharda University, Greater Noida, India. With over 20 years of professional experience in academia and research, he has developed expertise in diverse domains including Federated Learning, Deep Learning, Influence Maximization, Wireless Networks, Information Security and Sentiment Analysis. His work focuses on leveraging data-driven intelligence and privacy-preserving AI to enhance cyber and information resilience, optimize decision-making, and improve the efficiency of large-scale social networks. His research extends to applying deep learning models for sentiment analysis, uncovering patterns in user-generated data to drive smarter, AI-powered insights.

He is the author of a book titled "AI-Powered Pricing: Transforming Business with Intelligent Pricing Models". His one patent is published and many are under processing. He has actively published in reputed international journals and conferences, showcasing his commitment to advancing the frontiers of federated learning and decentralized AI models. His work aims to bridge the gap between cutting-edge AI research and real-world applications, particularly in secure and scalable machine learning for the finance industry and sustainable decision-making.Beyond research, He engages in academic collaborations, workshops, and professional forums, contributing to innovation and translational research. His dedication to interdisciplinary innovation continues to shape emerging trends in intelligent systems and federated learning.