Federated Artificial Intelligence Systems for Achieving Seamless Interoperability and Robust Performance in Multi-Cloud Frameworks
Keywords:
Federated AI, multi-cloud interoperability, decentralized systems, robust performance, cloud computing, data sovereignty, secure AI frameworksAbstract
As multi-cloud environments gain traction across industries, achieving seamless interoperability and robust performance has become a critical challenge. Federated Artificial Intelligence (AI) systems provide a decentralized approach to managing AI models across diverse cloud platforms while preserving data sovereignty and ensuring security. This paper explores the integration of federated AI systems into multi-cloud frameworks, emphasizing their ability to achieve scalable, secure, and interoperable solutions. Through a comprehensive literature review and analysis of existing frameworks, this paper highlights the challenges and strategies for optimizing federated AI in multi-cloud ecosystems. Data-driven insights and empirical results underscore the benefits and limitations of these systems in practice.
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Copyright (c) 2023 Priyanka Prajapat (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
