A review of federated learning approaches for predictive modeling and confidential data analysis in lending and borrowing behavior across decentralized financial networks

The rise of decentralized financial (DeFi) networks has revolutionized lending and borrowing practices, enabling peer-to-peer transactions without centralized intermediaries. However, analyzing user behavior within these ecosystems poses significant challenges due to the sensitive nature of financial data and regulatory demands for privacy preservation. Federated Learning (FL) has emerged as a promising machine learning paradigm that enables collaborative model training across distributed data sources without exposing raw data, thus aligning with the privacy-first requirements of DeFi environments. This review explores the state-of-the-art applications of FL in modeling lending and borrowing behavior across decentralized platforms. We examine the architectural frameworks, privacy-enhancing techniques (e.g., differential privacy, secure aggregation), and communication optimization strategies employed in current FL systems. The paper also highlights the unique challenges of implementing FL in DeFi, including data heterogeneity, malicious participant risk, scalability, and smart contract integration. By synthesizing recent advancements and identifying key research gaps, this review provides a comprehensive foundation for future studies aiming to enhance predictive accuracy, user trust, and regulatory compliance in decentralized financial analytics.