Federated AI Model Deployment Across Multi-Cloud Microservices: Architecture, Challenges, and Security Frameworks
Keywords:
Federated learning, multi-cloud architecture, microservices, model orchestration, security frameworks, zero-trust, containerizationAbstract
Purpose: This paper explores how federated AI models are deployed in multi-cloud environments using microservice architecture, identifying architectural solutions, security frameworks, and practical challenges.
Design/methodology/approach: A descriptive design is used, analyzing key technological integrations, deployment models, and referencing prior foundational work in multi-cloud and federated computing before 2016.
Findings: Deploying federated AI in distributed, cloud-native ecosystems introduces complexities in orchestration, heterogeneity management, and security enforcement.
Practical implications: Enterprises can leverage this architecture to build scalable and privacy-preserving AI systems, though secure communication and compliance across providers remain ongoing challenges.
Originality/value: This paper bridges classical multi-cloud security and architecture research with modern federated AI deployment needs, providing a unified framework that addresses both legacy and current gaps.
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Copyright (c) 2024 Hilary Mantel Ishiguro, (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
