Federated Learning Frameworks for Privacy-Preserving Business Intelligence in Cloud Ecosystems

Abstract

The paper presents a Federated Learning Framework for Privacy-Preserving Business Intelligence (BI) within multi-cloud environments, addressing the challenges of secure data collaboration across distributed enterprises. Traditional centralized BI architectures often violate privacy regulations and expose sensitive data, whereas the proposed federated approach enables collaborative analytics without raw data sharing. The framework integrates differential privacy, homomorphic encryption, and secure multi-party computation to ensure compliance with GDPR and HIPAA while maintaining high analytical utility. A multi-layered architecture—comprising the Data Layer, Federated Learning Layer, Privacy Layer, and BI Visualization Layer—was implemented using TensorFlow Federated and Posit. Experimental evaluations conducted on synthetic and real-world retail and financial datasets demonstrate superior performance over baseline models. The proposed system achieved 95.4% accuracy, a 23% reduction in communication overhead, and a privacy loss (ε) below 1.0. The results validate that privacy-preserving BI can be achieved without sacrificing analytical depth or scalability. The study establishes a robust, regulation-compliant paradigm for next-generation cloud BI systems that harmonize security, performance, and interpretability in enterprise decision-making.