Federated Learning Enabled Intelligent Cloud Security Architecture for Privacy-Preserving Enterprise Data Collaboration

Abstract

Enterprise organizations increasingly depend on cloud computing to store, process, and exchange large volumes of data across departments, business units, partners, and geographically distributed environments. Although cloud platforms enable scalable collaboration, centralized data sharing creates substantial privacy, security, regulatory, and governance challenges. Federated learning (FL) provides an alternative approach by enabling multiple organizations or distributed data owners to collaboratively train machine-learning models without directly transferring their raw data to a central repository. This research proposes a federated learning-enabled intelligent cloud security architecture for privacy-preserving enterprise data collaboration. The proposed architecture combines federated learning, secure aggregation, encryption, identity and access management, privacy-enhancin g mechanisms, intrusion detection, anomaly detection, cloud security monitoring, and intelligent risk assessment. Participating organizations retain their sensitive datasets locally while sharing model updates with a coordinating platform. Advanced machine-learning techniques are employed to detect abnormal behavior and security threats across distributed cloud environments, while privacy mechanisms reduce the possibility of reconstructing sensitive information from exchanged model parameters. The research adopts a design science and experimental methodology involving architecture development, federated model implementation, simulated enterprise participants, security-threat scenarios, and comparative evaluation against centralized machine-learning approaches. Performance is assessed using detection accuracy, precision, recall, F1-score, communication overhead, convergence time, privacy protection, computational cost, and resilience against adversarial attacks. The expected contribution is a secure and scalable architecture that enables organizations to benefit from collective intelligence without requiring unrestricted exchange of sensitive enterprise data.