Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT

TL;DR

This paper proposes a federated hyperdimensional computing framework tailored for resource-constrained IIoT devices, enabling efficient, collaborative, and resilient data analytics with reduced communication overhead.

Contribution

It introduces a novel federated HDC approach that significantly reduces communication costs and enhances learning efficiency in resource-limited IIoT environments.

Findings

Supports fast convergence in federated learning

Reduces communication overhead significantly

Demonstrates robustness in resource-constrained settings

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.