On-Device Inference for Assistive Applications: Accuracy, Battery and the Privacy Argument
DOI:
https://doi.org/10.5281/zenodo.22660797Keywords:
On-Device Inference for Assistive Applications; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
On-Device Inference for Assistive Applications is an area of active development in applied machine learning, with evidence that
has accumulated unevenly across its component questions. We review the current evidence base for on-device inference for
assistive applications, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice
has converged and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption.
We conclude that selective, context-specific use is best supported by the evidence currently available, rather than uniform
adoption or avoidance.
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