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<p>This paper introduces a holistic and scalable framework for optimizing Large Language Models (LLMs) in distributed environments, addressing three critical challenges: computational efficiency, ethical fairness, and governance. As LLMs scale, issues, such as excessive resource consumption, fairness violations, and limited transparency, hinder their broader deployment in real-world applications. We propose a novel three-tier architecture that integrates topology-aware parallelism, communication-efficient gradient aggregation, and memory-aware rematerialization. Our implementation reduces training time by 38% and memory usage by 42% on a 512-GPU A100 cluster, without compromising accuracy. To promote fairness, we incorporate a real-time adversarial debiasing module that reduces demographic AUC gaps by over 60% across gender, ethnicity, and religion. For model interpretability, we introduce a symbolic explainability engine that converts attention weights into transparent rule-based explanations, achieving 89.2% user satisfaction and outperforming Grad-CAM and vanilla attention. Furthermore, a lightweight governance layer aligned with ISO/IEC 27001 and ISO/IEC 23894 standards ensures traceability, audit logging, and policy enforcement throughout the model lifecycle. We validate our framework across diverse datasets, including C4, WikiText-103, RealNews, and BookCorpus, demonstrating low-latency drift and consistent fairness across domains. Comparative benchmarks against DeepSpeed, FairScale, and Megatron-LM show superior throughput, energy efficiency, and transparency. This work advances the foundation for ethical, efficient, and regulation-compliant LLM deployment at scale.</p>

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Optimizing Large Language Models in Distributed Environments: A Holistic Approach to Efficiency, Ethics, and Governance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月11日

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  • Mubarak Ahmad,
  • Abdulkadhem A. Abdulkadhem,
  • Umar Islam,
  • Hathal Salamah Alwageed,
  • Hanif Ullah,
  • A. Abdullah

摘要

This paper introduces a holistic and scalable framework for optimizing Large Language Models (LLMs) in distributed environments, addressing three critical challenges: computational efficiency, ethical fairness, and governance. As LLMs scale, issues, such as excessive resource consumption, fairness violations, and limited transparency, hinder their broader deployment in real-world applications. We propose a novel three-tier architecture that integrates topology-aware parallelism, communication-efficient gradient aggregation, and memory-aware rematerialization. Our implementation reduces training time by 38% and memory usage by 42% on a 512-GPU A100 cluster, without compromising accuracy. To promote fairness, we incorporate a real-time adversarial debiasing module that reduces demographic AUC gaps by over 60% across gender, ethnicity, and religion. For model interpretability, we introduce a symbolic explainability engine that converts attention weights into transparent rule-based explanations, achieving 89.2% user satisfaction and outperforming Grad-CAM and vanilla attention. Furthermore, a lightweight governance layer aligned with ISO/IEC 27001 and ISO/IEC 23894 standards ensures traceability, audit logging, and policy enforcement throughout the model lifecycle. We validate our framework across diverse datasets, including C4, WikiText-103, RealNews, and BookCorpus, demonstrating low-latency drift and consistent fairness across domains. Comparative benchmarks against DeepSpeed, FairScale, and Megatron-LM show superior throughput, energy efficiency, and transparency. This work advances the foundation for ethical, efficient, and regulation-compliant LLM deployment at scale.