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

This study proposes a unified framework for enhancing distributed AI systems by integrating Reinforcement Learning (RL), scalable indexing, Large Language Model (LLM) orchestration, and autonomous decision-making within federated cloud architectures. As distributed AI deployments grow in scale and complexity, the need for intelligent adaptability, efficient data management, and decentralized control becomes critical. The proposed architecture leverages RL agents to dynamically optimize task allocation, indexing strategies, and LLM invocation policies across heterogeneous nodes, enabling context-aware, resource-efficient operations. A series of experiments conducted in a simulated federated environment revealed that RL-enhanced systems achieved up to 111% improvement in cumulative rewards, 39% reduction in latency, and over 50% increase in throughput compared to baseline methods. Adaptive indexing driven by RL improved precision and retrieval efficiency, while LLM integration under RL control yielded faster response times and higher semantic accuracy with lower CPU overhead. The autonomous decision-making module demonstrated significant gains in accuracy and robustness, reducing convergence time and operational failures. These findings validate the efficacy of RL in orchestrating complex, real-time AI processes in distributed environments and highlight its potential to enable scalable, intelligent, and resilient cloud-native infrastructures for diverse applications. [ABSTRACT FROM AUTHOR]