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.
@article{arxiv.2306.01339,
title = {Resource-Efficient Federated Hyperdimensional Computing},
author = {Nikita Zeulin and Olga Galinina and Nageen Himayat and Sergey Andreev},
journal= {arXiv preprint arXiv:2306.01339},
year = {2023}
}
Comments
Accepted to Federated Learning Systems (FLSys) workshop, in Conjunction with the 6th MLSys Conference (MLSys 2023)