Edge-computing
157 papers with code • 0 benchmarks • 0 datasets
Deep Learning on EDGE devices
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Most implemented papers
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
dual-grp/DONE
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In this work, we propose DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning.
Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval
Extremely high data rates at modern synchrotron and X-ray free-electron laser light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes.
Supervised Compression for Resource-Constrained Edge Computing Systems
yoshitomo-matsubara/supervised-compression
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There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.
HRNET: AI on Edge for mask detection and social distancing
code-implementation1/Code4
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The framework further equips government agency, system providers to design and constructs technology-oriented models in community setup to Increase the quality of life using emerging technologies into smart urban environments.
Hardware-Efficient Deconvolution-Based GAN for Edge Computing
Azzam-Alhussain/QDCGAN-PYNQ
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Generative Adversarial Networks (GAN) are cutting-edge algorithms for generating new data samples based on the learned data distribution.
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge
esrgv3/qnn-adversarial-robustness
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To fill this gap, we empirically evaluate the effectiveness of attacks and defenses from (full-precision) ANNs on (constrained) QNNs.
A Quantization-Friendly Separable Convolution for MobileNets
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.
B-DCGAN:Evaluation of Binarized DCGAN for FPGA
We are trying to implement deep neural networks in the edge computing environment for real-world applications such as the IoT(Internet of Things), the FinTech etc., for the purpose of utilizing the significant achievement of Deep Learning in recent years.
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
IBM/adaptive-federated-learning
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Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches.
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models.
