Reinforcement Learning for Distributed AI Systems: Scalable Indexing,  LLM Integration, and Autonomous Decision-Making in Federated Cloud  Architectures

Rohith Narasimhamurthy, Sushant Mehta, Rajiv Kishore Gadda

Reinforcement Learning for Distributed AI Systems: Scalable Indexing, LLM Integration, and Autonomous Decision-Making in Federated Cloud Architectures

Authors

Rohith Narasimhamurthy, Sushant Mehta, Rajiv Kishore Gadda

Keywords:

Reinforcement Learning, Distributed AI Systems, Scalable Indexing, LLM Integration, Autonomous Decision-Making, Federated Cloud Architectures

Abstract

This study proposes a unified framework for enhancing distributed AI systems by integratingReinforcement Learning (RL), scalable indexing, Large Language Model (LLM) orchestration,and autonomous decision-making within federated cloud architectures. As distributed AI

References

Adam, M., & Baroud, U. (2024). Federated Learning For IoT: Applications, Trends, Taxonomy, Challenges, Current Solutions, and Future Directions. IEEE Open Journal of the Communications Society.

Alzoubi, Y. I., Mishra, A., & Topcu, A. E. (2024). Research trends in deep learning and machine learning for cloud computing security. Artificial Intelligence Review, 57(5), 132.

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Published

2025-07-30

How to Cite

Rohith Narasimhamurthy, Sushant Mehta, Rajiv Kishore Gadda. (2025). Reinforcement Learning for Distributed AI Systems: Scalable Indexing, LLM Integration, and Autonomous Decision-Making in Federated Cloud Architectures . Journal of Computational Analysis and Applications (JoCAAA), 34(7), 367–380. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3372