Federated Hyperdimensional Computing for Resource-Constrained Industrial IoT
This addresses the problem of enabling collaborative learning for edge devices with limited memory, compute, and bandwidth in Industrial IoT, representing an incremental improvement by integrating existing methods.
The paper tackles the challenge of deploying advanced data analytics like predictive maintenance in resource-constrained Industrial IoT by proposing federated hyperdimensional computing, which reduces communication overhead and achieves fast convergence speed and communication efficiency.
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.