As financial institutions increasingly rely on AI and ML for data-driven decision-making, concerns about data privacy, security, and regulatory compliance are growing. Federated learning (FL) emerges as a key solution, enabling collaborative AI model training across multiple organizations without sharing raw data. This book explores advancements in FL technology in the financial industry, specifically in the context of privacy-preserving AI. It also examines the significant shift from traditional centralized machine-learning approaches to decentralized learning techniques.
Structured into four comprehensive sections, the book offers an in-depth examination of the subject. The first section provides an overview and introduction to FL, and reviews the increasing challenges of data privacy and regulatory constraints in the financial industry, highlighting how federated learning enables secure AI-driven financial services. The second section explores federated architectures, secure multi-party computation, differential privacy, and homomorphic encryption. The third section highlights practical applications of AI and federated learning in areas such as risk management, fraud detection, credit scoring, and customer personalization, demonstrating how FL enhances security, scalability, and operational efficiency in financial systems. Financial applications where federated learning enhances security, scalability, and efficiency are also addressed. The fourth section discusses emerging trends in federated learning, including blockchain-based federated learning, zero-trust architectures, and its integration with decentralized finance (DeFi). The book concludes by examining practical implementations and regulatory considerations, ensuring compliance with data protection laws such as GDPR and CCPA.