Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks

TL;DR

This paper introduces a hyperdimensional computing-based method using randomized neural networks for efficient distributed classification, employing lossy compression to improve accuracy and reduce computational costs in decentralized data scenarios.

Contribution

It presents a novel approach combining hyperdimensional computing and randomized neural networks for distributed classification with lossy compression, enhancing efficiency and accuracy.

Findings

Higher accuracy than local classifiers

Approaches centralized classifier performance

Effective in decentralized data environments

Abstract

In the supervised learning domain, considering the recent prevalence of algorithms with high computational cost, the attention is steering towards simpler, lighter, and less computationally extensive training and inference approaches. In particular, randomized algorithms are currently having a resurgence, given their generalized elementary approach. By using randomized neural networks, we study distributed classification, which can be employed in situations were data cannot be stored at a central location nor shared. We propose a more efficient solution for distributed classification by making use of a lossy compression approach applied when sharing the local classifiers with other agents. This approach originates from the framework of hyperdimensional computing, and is adapted herein. The results of experiments on a collection of datasets demonstrate that the proposed approach has usually higher accuracy than local classifiers and getting close to the benchmark - the centralized classifier. This work can be considered as the first step towards analyzing the variegated horizon of distributed randomized neural networks.