Cloud-Native Distributed AI: Enabling Secure Collaborative Learning on Heterogeneous Infrastructure

• Shiva Kumar Bommakanti

  Author

This paper presents a foundational reference architecture for a cloud-native platform engineered to facilitate decentralized, privacy-preserving artificial intelligence model development. Rather than proposing a novel framework, this work synthesizes existing federated learning (FL) systems into a cohesive architectural blueprint that addresses the critical need for secure, collaborative intelligence without direct data centralization. The paper's primary technical contribution lies in its layered decomposition of distributed FL components, spanning control planes, execution engines, and secure communication channels, and their integration with cloud-native orchestration principles. The discussion emphasizes scalable operational capabilities across heterogeneous cloud infrastructure, detailing secure provisioning mechanisms and adaptable communication patterns. The described system is poised to transform complex collaborative AI tasks from isolated development into practical, cloud-native deployments, highlighting its significance for enterprise-grade distributed AI solutions.

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