Several recent works have proposed fully edge-based distributed training systems for situations when the communication to cloud is unstable or intermittent.
Duration: 13:52
Posted: Jun 8, 2023
Posted: Jun 8, 2023
Our container-based emulation and real device experiments demonstrate that TrustMe achieves up to 12% higher accuracy and 45% less training time compared to ...
Each sub-swarm is responsible for training a component network. The architecture of each component neural network in the ensemble is automatically configured.
Dec 17, 2022 · Trustworthy Distributed Deep Neural Network Training in an Edge Device Network. Sudipta Saha Shubha 1. ,. Haiying Shen 1. Show full list: 2 ...
In implementing a DDNN, we map sections of a DNN onto a distributed computing hierarchy. By jointly training these sections, we minimize communication and ...
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Edge computing is a distributed computing framework that brings applications closer to data sources such as IoT devices, local end devices, or edge servers.
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In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is ...
Training CNNs requires additional memory compared to inference due to the need to store input data, gradients, and activation values for each layer.
In this paper, we review three major research areas for on-device computation, specifically quantization, pruning, and network architecture design.
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