将大模型拆分蒸馏，让物联网设备本地化部署智能模型。
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions
- 分拆知识蒸馏框架，结合分片学习实现模型压缩与隐私保护。
- 在边缘设备上实现低延迟、低能耗的模型训练与推理。
- 适合资源受限且注重数据隐私的物联网场景应用。
大型模型（LMs）在物联网（IoT）系统中具有巨大潜力，可支持智能语音助手、预测性维护和医疗监测等应用。然而，在边缘服务器上训练大模型会引发数据隐私问题，而直接在物联网设备上部署则受限于计算和内存资源。本文分析了物联网系统中训练大模型的关键挑战，包括能源限制、延迟要求和设备异构性，并提出动态资源管理、自适应模型分片及集群协同训练等解决方案。此外，提出一种分拆知识蒸馏框架，将大模型高效压缩为可在物联网设备上部署的小型模型，同时确保原始数据保留在本地。该框架融合知识蒸馏与分片学习，显著降低能耗并满足低模型训练延迟需求。通过案例研究验证了所提框架的可行性与性能。
原文摘要 · Abstract (English)
Large models (LMs) have immense potential in Internet of Things (IoT) systems, enabling applications such as intelligent voice assistants, predictive maintenance, and healthcare monitoring. However, training LMs on edge servers raises data privacy concerns, while deploying them directly on IoT devices is constrained by limited computational and memory resources. We analyze the key challenges of training LMs in IoT systems, including energy constraints, latency requirements, and device heterogeneity, and propose potential solutions such as dynamic resource management, adaptive model partitioning, and clustered collaborative training. Furthermore, we propose a split knowledge distillation framework to efficiently distill LMs into smaller, deployable versions for IoT devices while ensuring raw data remains local. This framework integrates knowledge distillation and split learning to minimize energy consumption and meet low model training delay requirements. A case study is presented to evaluate the feasibility and performance of the proposed framework.
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