adversarial attack detection
blockchain integration finance
Category=KFF
Category=URH
Category=UT
Category=UYQ
distributed machine learning
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_new_release
eq_nobargain
eq_non-fiction
financial data security
quantum cybersecurity financial systems
regulatory compliance finance
zero trust architecture
Product details
- ISBN 9781041115106
- Weight: 790g
- Dimensions: 178 x 254mm
- Publication Date: 12 May 2026
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
Secure
checkout
Fast Shipping
Easy returns
Federated Learning in Finance: Unlocking Privacy-Preserving and Cyber Resilience using AI is an edited volume designed to explore 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 artificial intelligence (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.
