Secure Multi-Cloud Data Intelligence through Federated Learning and Predictive Analytics for Enterprise Systems
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806045Keywords:
Federated Learning, Multi-Cloud Computing, Predictive Analytics, Enterprise Systems, Data Intelligence, Cloud Security, Privacy-Preserving Machine Learning, Distributed Intelligence, Data Governance, Secure ComputingAbstract
Enterprise organizations increasingly operate across multiple cloud platforms to achieve scalability, flexibility, availability, and cost efficiency. However, the distribution of organizational data across heterogeneous cloud environments creates substantial challenges related to privacy, security, interoperability, governance, and intelligent data analysis. Traditional centralized machine-learning approaches require data to be collected and transferred to a common processing environment, potentially increasing exposure of sensitive business information. Federated learning provides an alternative by enabling multiple participating systems to collaboratively train machine-learning models while keeping their underlying datasets within their respective environments. When combined with predictive analytics, federated learning can support enterprise intelligence without requiring extensive movement of sensitive data between cloud providers. This study examines a secure multi-cloud data intelligence framework that integrates federated learning, predictive analytics, privacy-preserving mechanisms, and cloud security controls. The proposed approach enables participating cloud environments to train local models using their own operational data and share model updates rather than raw information. A coordinating layer aggregates these updates to construct a collaborative global model, while security mechanisms protect communication, participant identity, and model integrity. Predictive analytics can subsequently be used for applications such as demand forecasting, anomaly detection, resource optimization, cybersecurity monitoring, and operational risk prediction. The study adopts a conceptual and qualitative research methodology based on literature analysis, framework development, and scenario-based evaluation. Particular attention is given to privacy, model poisoning, communication security, heterogeneous data, scalability, and governance. The research argues that integrating federated learning with predictive analytics can establish a foundation for secure, distributed, and intelligent enterprise decision-making across multi-cloud environments
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