Federated Generative AI for Personalized Decision-Making in Multi-Cloud Ecosystems: A Kubernetes-Based Zero Trust Approach

Abstract

In the era of multi-cloud ecosystems, personalized decision-making has become increasingly essential for optimizing resource utilization, enhancing security, and improving user experiences. Federated Generative AI offers a promising solution by enabling collaborative learning across distributed data sources while preserving privacy. This paper presents a novel approach that integrates Federated Generative AI with Kubernetes to create a Zero Trust Architecture (ZTA) for personalized decision-making in multi-cloud environments. Our framework leverages the federated learning paradigm to train generative models across different cloud providers, ensuring data privacy and security. The Kubernetes-based orchestration facilitates seamless deployment, scaling, and management of AI workloads while maintaining strict access controls inherent in the Zero Trust model. We demonstrate how this architecture can effectively handle heterogeneous cloud infrastructures, provide real-time decision-making, and mitigate risks associated with data breaches. The proposed method is evaluated through extensive experiments, showcasing its effectiveness in personalized decision-making tasks with high accuracy, low latency, and robust security. This research offers a blueprint for secure, scalable, and efficient multi-cloud AI systems, paving the way for advanced applications in healthcare, finance, and other sectors requiring tailored decision support.