Web: http://arxiv.org/abs/2206.01366

June 20, 2022, 1:13 a.m. | Taehyeon Kim, Se-Young Yun

cs.CV updates on arXiv.org arxiv.org

Efficient deployment of deep neural networks across many devices and resource
constraints, especially on edge devices, is one of the most challenging
problems in the presence of data-privacy preservation issues. Conventional
approaches have evolved to either improve a single global model while keeping
each local training data decentralized (i.e., data-heterogeneity) or to train a
once-for-all network that supports diverse architectural settings to address
heterogeneous systems equipped with different computational capabilities (i.e.,
model-heterogeneity). However, little research has considered both directions
simultaneously. …

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