FEDERATED LEARNING TECHNIQUES FOR SECURE AI MODEL TRAINING IN FINTECH

The rapid digitalization of financial services has driven the adoption of artificial intelligence (AI) for automating decision-making, fraud detection, risk assessment, and personalized financial offerings. However, these AI models often require access to sensitive user data, leading to significant concerns around data privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as a transformative approach to address these concerns by enabling collaborative model training across decentralized data sources without exposing raw data to central servers. This paper explores the application of federated learning techniques specifically tailored for secure AI model training in the FinTech domain. We present an in-depth analysis of FL architecture, system design considerations, privacy-preserving mechanisms, and optimization strategies for heterogeneous and non-IID data distributions. Real-world use cases are studied to demonstrate the feasibility of integrating FL with existing financial systems. The evaluation also compares FL’s effectiveness against centralized models, considering parameters like model accuracy, data leakage resistance, and convergence performance. This work aims to provide FinTech developers and researchers with a practical blueprint for implementing secure and scalable federated AI systems that meet evolving data protection standards.