Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT

Nikita Zeulin; Olga Galinina; Nageen Himayat; Sergey Andreev

cs.LGMar 20, 2026

Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT

Authors: Nikita Zeulin, Olga Galinina, Nageen Himayat, Sergey Andreev

Organizations: Tampere University, Tampere, Finland · Tampere Institute for Advanced Study, Tampere University, Finland · Intel Corporation, Santa Clara, CA, United States

Abstract

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.

Figures & tables

Fig. 1 : Illustration of envisioned industrial IoT system.

Fig. 2 : Architecture of proposed resource-efficient federated HDC framework for resource-constrained devices. Edge devices encode local data into hypervectors and update class prototypes. Instead of transmitting full model parameters, devices send compact prototype vectors to a central server for aggregation.

Panel of Figure 3 — Fig. 3 : Illustrative comparison of maximum accuracy achieved by baseline and proposed federated HDC methods.

Fig. 4 : Total uplink traffic consumption to achieve baseline federated HDC accuracy.

Fig. 5 : Comparison of maximum accuracy and volume of uplink traffic between proposed method and baseline for non-i.i.d. scenario.

Industrial Internet of Things

See all 16 papers

Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks
May 29, 2026Kangmin Kim, Jaeyoung SongEnergy-Efficient ML Internet of Things
School of Electrical and Electronics Engineering, Pusan National University, Busan, 46241, Korea
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications
Dec 9, 2024Jiechao Gao, Yuangang Li, Jie Wang +3Hierarchical Federated Learning Internet of Things
University of Virginia Charlottesville, VA, USA
Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks
Apr 12, 2026Haihui Xie, Wenkun Wen, Shuwu Chen +2Federated Learning Energy-Efficient ML
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, and also with the Engineering Research Center of Smart Sensing and Agricultural Chip Technology, Fujian Province University, Fuzhou 350002, China · R&D Department, Techphant Technologies Company Ltd., Guangzhou 510310, China · School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China

May 29, 2026J/K move · Enter open · S save

Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks

Kangmin Kim, Jaeyoung Song

School of Electrical and Electronics Engineering, Pusan National University, Busan, 46241, Korea

Energy-Efficient ML Internet of Things Wireless Resource Allocation Industrial Internet of Things Federated Learning

Open paper AskPDF arXiv

We consider a federated learning (FL) system in which Industrial Internet-of-Things (IIoT) devices collaboratively train a global model over wireless channels without sharing local data. In such systems, communication time is a primary bottleneck that constrains overall training efficiency. Unlike conventional networks that prioritize individual quality-of-service requirements, FL systems collectively aim to converge to an optimal global model as efficiently as possible, which calls for a fundamentally different approach to bandwidth allocation. In this paper, we propose a novel bandwidth allocation policy that exploits the heterogeneity of device computing capabilities to minimize total training time. Rather than distributing bandwidth among all selected devices simultaneously, the proposed policy partitions the participating devices into ordered subsets and sequentially grants each subset exclusive access to the full bandwidth. We formally prove that this partitioning-based policy achieves a strictly lower training time than any bandwidth allocation scheme without partitioning, irrespective of the underlying scheduling algorithm. Furthermore, by reducing per-device transmission duration, the proposed policy also minimizes uplink energy consumption, which is particularly beneficial for battery-constrained IIoT devices. Extensive experiments on real-world datasets - including GC10-Det, an industrial surface defect benchmark, and CIFAR-10, a standard image classification benchmark - demonstrate that the proposed policy consistently reduces training time and energy consumption compared to existing bandwidth allocation schemes, approaching the theoretical lower bound on round time.

Abstract

Industrial Internet of Things

Explore similar work

Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks

H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications

Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks