Designing Federated Learning Driven Multi-Cloud Architecture for Secure Financial Data Exchange and Intelligent Risk Analytics
DOI:
https://doi.org/10.15662/IJARCST.2025.0806031Keywords:
Federated Learning, Multi-Cloud Architecture, Financial Data Security, Intelligent Risk Analytics, Differential Privacy, Zero-Trust Architecture, Cross-Border Data SovereigntyAbstract
Financial institutions increasingly face a critical dilemma: the imperative to leverage collaborative data analytics for advanced risk modeling versus the strict regulatory mandates governing data privacy, sovereignty, and security. Traditional centralized analytics architectures require sensitive cross-border and cross-organizational data pooling, exposing institutions to severe data breach risks and non-compliance penalties. To solve this, this paper proposes a novel, privacy-preserving Multi-Cloud Federated Learning (MC-FL) architecture tailored for financial ecosystems. By integrating federated learning with multi-cloud deployments, local financial entities can train global machine learning models—such as deep neural networks and gradient-boosted trees—on decentralized datasets without raw financial data ever leaving private cloud boundaries. The framework incorporates differential privacy, secure multi-party computation, and zero-trust orchestration across heterogeneous cloud environments (AWS, Azure, Google Cloud Platform) to prevent inference attacks, parameter leakage, and single-point-of-failure vulnerabilities. Intelligent risk analytics, including real-time fraud detection and credit scoring, are computed via distributed edge-cloud aggregation nodes. Empirical evaluations demonstrate that the proposed MC-FL architecture achieves predictive accuracy comparable to centralized models while maintaining strict regulatory compliance (GDPR, CCPA, PCI-DSS), significantly mitigating cross-cloud latency, and ensuring high fault tolerance in dynamic, cross-institutional financial data exchange networks.
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