Enhancing Computational Efficiency in Distributed Learning Models Using Artificial Intelligence within Cloud Computing Environments

Abstract

Cloud computing, coupled with distributed learning, has revolutionized data-intensive applications. However, achieving computational efficiency remains a critical challenge, especially under resource constraints. This paper explores how artificial intelligence (AI) can enhance the computational performance of distributed learning models in cloud environments. By examining various AI-driven techniques such as federated learning, workload optimization, and energy-efficient task scheduling, we identify core strategies to reduce latency and improve resource allocation. A comparative analysis of recent innovations demonstrates a significant leap in operational efficiency, providing insight for future system architectures.