Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT

2026-03-20

AI Summary

This work addresses the challenge of deploying advanced data analytics on resource-constrained edge devices in industrial IoT (IIoT) environments by introducing hyperdimensional computing (HDC) into a federated learning (FL) framework for the first time. The proposed approach enables collaborative learning through the exchange of high-dimensional prototype representations rather than full model parameters, substantially reducing communication overhead and computational burden while accelerating convergence. Experimental results demonstrate that the method achieves fast, robust, and lightweight distributed intelligence with high energy efficiency, making it well-suited for large-scale IIoT applications.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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.

Problem

• Industrial Internet of Things
• resource-constrained
• edge devices
• data analytics
• communication overhead

Innovation

• Federated Learning
• Hyperdimensional Computing
• Industrial IoT
• Communication Efficiency
• Resource-Constrained Edge Devices