Designing Federated Learning Enabled Multi-Cloud Cyber Defense Framework for Intelligent Enterprise Threat Intelligence

Abstract

The rapid adoption of multi-cloud computing has transformed enterprise information technology infrastructures by enabling organizations to leverage the strengths of multiple cloud service providers for improved scalability, resilience, and operational flexibility. However, the distributed nature of multi-cloud environments has introduced significant cybersecurity challenges, including inconsistent security policies, fragmented visibility, sophisticated cyberattacks, and complex threat management. Traditional centralized cybersecurity approaches often require extensive data sharing, raising concerns regarding privacy, regulatory compliance, and organizational confidentiality. Federated Learning (FL) has emerged as a promising distributed artificial intelligence paradigm that enables collaborative model training across multiple organizations without exchanging sensitive data. This research proposes a Federated Learning-enabled multi-cloud cyber defense framework to enhance intelligent enterprise threat intelligence through privacy-preserving collaborative learning, real-time anomaly detection, automated threat intelligence sharing, and adaptive security decision-making. The study adopts a qualitative and conceptual research methodology supported by comprehensive literature analysis to examine existing cybersecurity frameworks and identify opportunities for integrating Federated Learning within multi-cloud security architectures. The proposed framework combines federated machine learning, cloud-native security services, threat intelligence platforms, security orchestration, and continuous monitoring to strengthen enterprise cyber resilience. The findings indicate that Federated Learning significantly improves collaborative threat detection, reduces privacy risks, enhances predictive intelligence, minimizes detection latency, and supports scalable cybersecurity operations across distributed cloud ecosystems.