Designing Federated Learning Systems for Collaborative Financial Analytics

Abstract

Federated Learning (FL) has emerged as a transformative paradigm for privacy-preserving collaborative machine learning, particularly in the financial sector, where data privacy and regulatory compliance are paramount. By enabling decentralized model training across distributed datasets without centralized data aggregation, FL addresses critical challenges in financial analytics, such as fraud detection, risk assessment, credit scoring, and cross-institutional insights. We will explore the principles, applications, and challenges of FL in finance, emphasizing its potential to enhance model robustness, ensure data sovereignty, and comply with stringent regulations like GDPR and anti-money laundering frameworks. Key challenges include data heterogeneity, secure aggregation techniques, regulatory alignment, and resistance to adversarial attacks. Case studies from banking, regulatory bodies, and financial intermediaries illustrate successful implementations, underscoring FL’s capacity to unlock collaborative insights while preserving confidentiality. The study concludes with design principles for scalable, secure FL systems and highlights future directions for adoption in global financial ecosystems.