An AI Enabled Federated Learning Architecture for Secure Large Scale Predictive Analytics across Finance and Healthcare

Abstract

The increasing volume and sensitivity of data in finance and healthcare have created an urgent need for predictive analytics that preserves privacy, supports cross‑institution collaboration, and scales to large‑scale distributed environments. Traditional centralized machine learning approaches require pooling data into a central repository, raising significant privacy, regulatory, and security concerns. Federated Learning (FL) addresses these challenges by enabling model training across distributed data sources without sharing raw data, thereby enhancing privacy and compliance.   This paper proposes a comprehensive AI‑Enabled Federated Learning Architecture tailored for secure large‑scale predictive analytics across finance and healthcare domains. The architecture integrates local model training at institutional edges, secure aggregation through encrypted communication, and a centralized orchestration layer for global model updates.   Core design features include robust authentication and authorization, differential privacy, homomorphic encryption options, and audit‑aware governance suitable for regulatory requirements like GDPR, HIPAA, and financial data regulations. We evaluate the architecture using real and synthetic datasets from both domains, focusing on predictive tasks such as credit risk assessment and disease outcome prediction.   The results demonstrate that the federated framework achieves predictive performance comparable to centralized models while significantly improving privacy preservation, lowering data transfer risk, and enhancing compliance. The paper concludes with observations on scalability, limitations, and avenues for future work.