1. Resource-Efficient Federated Hyperdimensional Computing
- Author
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Zeulin, Nikita, Galinina, Olga, Himayat, Nageen, and Andreev, Sergey
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing ,Distributed, Parallel, and Cluster Computing (cs.DC) ,Machine Learning (cs.LG) - 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., Accepted to Federated Learning Systems (FLSys) workshop, in Conjunction with the 6th MLSys Conference (MLSys 2023)
- Published
- 2023