Resource-Efficient Federated Hyperdimensional Computing

TL;DR

This paper introduces RE-FHDC, a resource-efficient federated hyperdimensional computing framework that trains multiple smaller models and refines them to achieve high performance with less resource consumption.

Contribution

The paper proposes a novel federated HDC framework that reduces resource usage while maintaining or improving predictive accuracy through a dropout-inspired refinement process.

Findings

Achieves comparable or higher accuracy than baseline federated HDC.

Consumes less computational and wireless resources.

Effectively trains multiple small HDC sub-models for improved efficiency.

Abstract

In conventional federated hyperdimensional computing (HDC), training larger models usually results in higher predictive performance but also requires more computational, communication, and energy resources. If the system resources are limited, one may have to sacrifice the predictive performance by reducing the size of the HDC model. The proposed resource-efficient federated hyperdimensional computing (RE-FHDC) framework alleviates such constraints by training multiple smaller independent HDC sub-models and refining the concatenated HDC model using the proposed dropout-inspired procedure. Our numerical comparison demonstrates that the proposed framework achieves a comparable or higher predictive performance while consuming less computational and wireless resources than the baseline federated HDC implementation.