智能软件化网络中的知识蒸馏：进展与开放挑战
Knowledge Distillation for Intelligent Softwarized Networks: Advances and Open Challenges
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中文总结 AI 辅助
本文综述知识蒸馏在智能软件化网络中的应用，分类分析现有工作，指出其集成零散、探索不足，并展望可扩展、自适应和能量感知蒸馏机制的开放挑战。
中文摘要 AI 辅助
软件定义网络和网络功能虚拟化的日益普及，加上机器学习（ML）的快速发展，正在推动云、边缘和分布式环境中向智能网络软件化的演进。然而，在这种异构环境中部署复杂的学习模型带来了延迟、计算和能耗方面的挑战。知识蒸馏（KD）作为一种有前景的方法应运而生，通过将知识从高容量教师模型迁移到紧凑的学生模型，实现轻量级高效的智能。尽管知识蒸馏在通用机器学习领域已被广泛研究，但其在智能软件化网络中的集成仍然零散且探索不足。本文回顾并分类了在软件化网络中融入知识蒸馏的最新工作，分析了当前趋势和局限性，并概述了面向可扩展、自适应和能量感知蒸馏机制的开放挑战与未来方向。
英文摘要
The increasing adoption of software defined networking and network function virtualization, combined with rapid advances in Machine Learning (ML), is driving the evolution toward intelligent network softwarization across cloud, edge, and distributed environments. However, deploying complex learning models in such heterogeneous environments introduces challenges in latency, computation, and energy consumption. Knowledge Distillation (KD) has emerged as a promising approach to enable lightweight and efficient intelligence by transferring knowledge from high-capacity teacher models to compact student models. Despite its extensive study in general ML domains, the integration of KD into intelligent softwarized networks remains fragmented and underexplored. In this paper, we review and classify recent efforts that incorporate KD within softwarized networks, analyze current trends and limitations, and outline open challenges and future directions toward scalable, adaptive, and energy-aware distillation mechanisms.
发表机构
- Université de Technologie de Compiègne CNRS, Heudiasyc UMR 7253（贡比涅技术大学）
- Université de Bordeaux, CNRS Bordeaux INP, LaBRI UMR 5800（波尔多大学）
机构由 AI 辅助整理，请以论文原文为准。
