用高维计算降低工业物联网的算力与通信负担。
Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT
- 将高维计算融入联邦学习，只传输原型表示。
- 相比传统方法，通信开销大幅降低，收敛速度快。
- 适合资源受限的工业物联网场景，部署更高效。
在工业物联网（IIoT）系统中，边缘设备常受内存、计算能力及无线带宽的严格限制，难以部署先进的数据分析任务，如预测性与处方性维护。本文探索高维计算（HDC）作为一种轻量级学习范式，适用于资源受限的IIoT环境。传统集中式HDC利用高维向量空间特性实现节能训练与推理。本文将其集成到联邦学习（FL）框架中，设备仅交换原型表示，显著降低通信开销。数值结果表明，联邦HDC在IIoT中支持协作学习，具备快速收敛速度与通信效率。这些结果表明，HDC是大规模、资源受限的IIoT环境中分布式智能的轻量且鲁棒的框架。
原文摘要 · Abstract (English)
In the Industrial Internet of Things (IIoT) systems, edge devices often operate under strict constraints in memory, compute capability, and wireless bandwidth. These limitations challenge the deployment of advanced data analytics tasks, such as predictive and prescriptive maintenance. In this work, we explore hyperdimensional computing (HDC) as a lightweight learning paradigm for resource-constrained IIoT. Conventional centralized HDC leverages the properties of high-dimensional vector spaces to enable energy-efficient training and inference. We integrate this paradigm into a federated learning (FL) framework where devices exchange only prototype representations, which significantly reduces communication overhead. Our numerical results highlight the potential of federated HDC to support collaborative learning in IIoT with fast convergence speed and communication efficiency. These results indicate that HDC represents a lightweight and resilient framework for distributed intelligence in large-scale and resource-constrained IIoT environments.
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