Privacy-Preserving Multi-Cloud Cybersecurity Intelligence using Federated Learning for Enterprise Environments Worldwide

Abstract

The rapid adoption of multi-cloud environments has transformed enterprise computing by enabling organizations to distribute applications, data, and services across multiple cloud providers. However, this distributed architecture creates significant cybersecurity and privacy challenges because security intelligence is fragmented across heterogeneous platforms, jurisdictions, and organizational boundaries. Privacy-preserving federated learning provides a promising approach by enabling participating entities to collaboratively train machine-learning models without directly exchanging sensitive raw security data. This paper examines a conceptual framework for applying federated learning to multi-cloud cybersecurity intelligence in enterprise environments worldwide. The proposed approach combines federated learning, secure aggregation, differential privacy, encryption, identity management, distributed threat detection, and cloud-native security monitoring. The methodology emphasizes collaborative model training across geographically distributed enterprise environments while maintaining local control over sensitive telemetry. Security events, network observations, authentication records, endpoint information, and application logs remain within their respective environments, while model updates are securely coordinated through a federated orchestration layer. The framework also considers heterogeneous data distributions, communication efficiency, adversarial participants, model poisoning, privacy leakage, regulatory requirements, and model convergence. Evaluation is proposed using detection accuracy, precision, recall, false-positive rates, communication overhead, training latency, privacy protection, and resilience against adversarial behavior. The study provides a foundation for developing globally distributed cybersecurity intelligence systems that improve collective threat-detection capabilities while reducing the need for centralized collection of sensitive enterprise security information.