Privacy-Preserving Scalable Enterprise Analytics using Federated Machine Learning across Distributed Clouds
DOI:
https://doi.org/10.15662/IJARCST.2025.0805040Keywords:
Federated Machine Learning, Privacy-Preserving Analytics, Distributed Cloud Computing, Enterprise Analytics, Secure Aggregation, Differential Privacy, Data Security, Cloud Computing, Machine Learning, Scalability, Data Governance, Confidential ComputingAbstract
The rapid adoption of cloud computing has enabled enterprises to collect, store, and analyze massive volumes of data across geographically distributed environments. However, centralized analytics creates significant privacy, security, regulatory, and data-governance challenges, particularly when organizational data cannot be transferred across jurisdictions, business units, or cloud providers. Federated Machine Learning (FML) provides an alternative paradigm in which machine-learning models are trained collaboratively across distributed data sources while sensitive raw data remains within local environments. This essay proposes a privacy-preserving and scalable enterprise analytics framework that integrates federated machine learning with distributed cloud infrastructures. The proposed approach combines decentralized data processing, secure model aggregation, differential privacy, encryption, access control, and adaptive resource orchestration to support trustworthy analytics across heterogeneous clouds. The methodology emphasizes data locality, communication efficiency, model convergence, privacy protection, scalability, and regulatory compliance. Experiments can evaluate the framework using heterogeneous enterprise datasets distributed across simulated or real cloud nodes, comparing centralized learning, conventional distributed learning, and federated approaches. Key performance indicators include prediction accuracy, training latency, communication overhead, computational cost, scalability, privacy leakage, and robustness against malicious participants. The study is expected to demonstrate that federated learning can provide competitive analytical performance while substantially reducing direct exposure of sensitive enterprise data. The proposed framework contributes to secure cross-cloud analytics by establishing a practical balance between privacy, scalability, computational efficiency, and model utility
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