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.

翻译：在传统联邦超维计算（HDC）中，训练较大模型通常能获得更高的预测性能，但同时也需要更多的计算、通信和能源资源。当系统资源受限时，用户可能需要通过减小HDC模型尺寸来牺牲预测性能。本文提出的资源高效联邦超维计算（RE-FHDC）框架通过训练多个较小的独立HDC子模型，并利用所提出的类丢弃（dropout）过程对拼接后的HDC模型进行优化，从而缓解了上述限制。数值比较表明，与基线联邦HDC实现相比，所提框架在消耗更少计算和无线资源的同时，能达到相当或更高的预测性能。