On-device Training: A First Overview on Existing Systems
- URL: http://arxiv.org/abs/2212.00824v2
- Date: Tue, 9 May 2023 08:16:27 GMT
- Title: On-device Training: A First Overview on Existing Systems
- Authors: Shuai Zhu, Thiemo Voigt, JeongGil Ko, Fatemeh Rahimian
- Abstract summary: Efforts have been made to deploy some models on resource-constrained devices as well.
This work targets to summarize and analyze state-of-the-art systems research that allows such on-device model training capabilities.
- Score: 8.0653715405809
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The recent breakthroughs in machine learning (ML) and deep learning (DL) have
catalyzed the design and development of various intelligent systems over wide
application domains. While most existing machine learning models require large
memory and computing power, efforts have been made to deploy some models on
resource-constrained devices as well. A majority of the early application
systems focused on exploiting the inference capabilities of ML and DL models,
where data captured from different mobile and embedded sensing components are
processed through these models for application goals such as classification and
segmentation. More recently, the concept of exploiting the mobile and embedded
computing resources for ML/DL model training has gained attention, as such
capabilities allow (i) the training of models via local data without the need
to share data over wireless links, thus enabling privacy-preserving computation
by design, (ii) model personalization and environment adaptation, and (ii)
deployment of accurate models in remote and hardly accessible locations without
stable internet connectivity. This work targets to summarize and analyze
state-of-the-art systems research that allows such on-device model training
capabilities and provide a survey of on-device training from a systems
perspective.
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