Resource-Efficient Federated Hyperdimensional Computing
Nikita Zeulin Olga Galinina Nageen Himayat Sergey Andreev
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.