Jan 7, 2022 · In this paper, an approach for distributing the deep neural network (DNN) training onto IoT edge devices is proposed.
In this paper, an approach for distributing the deep neural network (DNN) training onto IoT edge devices is proposed. The approach results in protecting ...
In this paper, an approach for distributing the deep neural network (DNN) training onto IoT edge devices is proposed. The approach results in protecting ...
In recent years new methodologies have emerged for training models in a distributed manner over edge devices, keeping the data on the devices themselves. This ...
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Delegating DNN training onto edge devices from the central cloud reduces communication costs and preserves data privacy on edge devices. The authors ...
Our approach, called RecycleML, uses cross modal transfer to accelerate the learning of edge devices across different sensing modalities.
Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of ...
This survey delves into Edge Learning (EL), specifically the optimization of ML model training at the edge.
In this report, we describe an approach for distributed CNN training exclu- sively on mobile and edge devices. Our approach is beneficial for the initial CNN.
Dec 13, 2022 · This article proposes EdgeMesh, a hybrid parallel training mode based on the Mesh-Tensorflow framework, consisting of an adaptive meshing strategy and a ...