Collaborative Learning for Distributed Cyber Defense in Multi-Cloud Environments

Abstract

The increasing adoption of multi-cloud architectures has transformed the landscape of enterprise computing, offering scalability, flexibility, and resilience. However, it has also expanded the attack surface and introduced new cybersecurity challenges. In such heterogeneous and distributed environments, traditional centralized security mechanisms are often insufficient to detect, respond to, and mitigate complex cyber threats. Collaborative learning—particularly Federated and Distributed Machine Learning—emerges as a powerful paradigm for enabling cooperative cyber defense without compromising data privacy. This paper explores how collaborative learning frameworks can facilitate real-time threat intelligence sharing, anomaly detection, and adaptive defense coordination across diverse cloud infrastructures. It discusses architectural models for secure collaboration, the role of AI in distributed threat analysis, and the integration of privacy-preserving technologies such as differential privacy and secure multi-party computation. Furthermore, the paper highlights challenges in scalability, trust management, and interoperability, providing insights into how collaborative intelligence can shape the future of resilient, autonomous, and privacy-aware cyber defense in multi-cloud ecosystems.