Federated Learning Architecture for Multi-Cloud Financial Analytics with Privacy-Preserving Data Intelligence

Abstract

The rapid digital transformation of the financial sector has resulted in the generation of massive volumes of sensitive customer and transactional data distributed across multiple cloud platforms. Traditional centralized machine learning approaches often require the transfer of confidential financial data to a single repository, increasing the risk of privacy breaches, regulatory non-compliance, and cyberattacks. Federated Learning (FL) has emerged as a promising distributed learning paradigm that enables collaborative model training without exposing raw data. This study presents a federated learning architecture designed for multi-cloud financial analytics with privacy-preserving data intelligence. The proposed architecture integrates secure aggregation, differential privacy, encryption techniques, and decentralized model synchronization to ensure secure collaboration among financial institutions operating across heterogeneous cloud environments. The architecture supports predictive analytics, fraud detection, credit risk assessment, customer behavior analysis, and anti-money laundering applications while maintaining data confidentiality and regulatory compliance. The research adopts a conceptual design supported by extensive literature analysis and system modeling to evaluate the effectiveness of federated learning in multi-cloud ecosystems. The proposed framework demonstrates improvements in privacy protection, scalability, interoperability, and model accuracy while minimizing communication overhead and security vulnerabilities. The findings suggest that federated learning offers a sustainable and secure solution for intelligent financial analytics in increasingly distributed cloud computing environments