Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917354851532800 |
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| author | Zeulin, Nikita Galinina, Olga Himayat, Nageen Andreev, Sergey |
| author_facet | Zeulin, Nikita Galinina, Olga Himayat, Nageen Andreev, Sergey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20037 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT Zeulin, Nikita Galinina, Olga Himayat, Nageen Andreev, Sergey Machine Learning Networking and Internet Architecture 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. |
| title | Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT |
| topic | Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2603.20037 |