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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.
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