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.

翻译：在工业物联网系统中，边缘设备通常在内存、计算能力和无线带宽方面受到严格约束。这些限制对预测性和规范性维护等高级数据分析任务的部署提出了挑战。本文探索超维计算作为资源受限工业物联网的轻量级学习范式。传统集中式超维计算利用高维向量空间的特性，实现高能效训练与推理。我们将该范式集成到联邦学习框架中，设备间仅交换原型表示，从而显著降低通信开销。数值结果表明，联邦超维计算能够以快速收敛速度和高效通信支持工业物联网中的协作学习。这些结果证实，超维计算为大规模资源受限工业物联网环境中的分布式智能提供了轻量级且具有鲁棒性的框架。