Edge-computing
163 papers with code • 0 benchmarks • 0 datasets
Deep Learning on EDGE devices
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Latest papers
Towards Decentralized Task Offloading and Resource Allocation in User-Centric Mobile Edge Computing
qlt315/ucmec-mmwave-fronthaul
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In the traditional cellular-based mobile edge computing (MEC), users at the edge of the cell are prone to suffer severe inter-cell interference and signal attenuation, leading to low throughput even transmission interruptions.
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
xingqiuhe/dpds
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In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used.
Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
Our framework features a two-stream encoder, an active fusion decoder (AFD) and a dual-task regularization approach.
FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion
Multi-satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion representation of complex high-dimensional data under limited computational resources.
Adversarial Machine Learning in Latent Representations of Neural Networks
Our experimental results support our theoretical findings by showing that the compressed latent representations can reduce the success rate of adversarial attacks by 88% in the best case and by 57% on the average compared to attacks to the input space.
DNNShifter: An Efficient DNN Pruning System for Edge Computing
Compared to sparse models, the pruned model variants are up to 5. 14x smaller and have a 1. 67x inference latency speedup, with no compromise to sparse model accuracy.
A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i. e., whether offloading or not) and computational resource allocation of MEC.
Cost-effective On-device Continual Learning over Memory Hierarchy with Miro
omnia-unist/Miro
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Continual learning (CL) trains NN models incrementally from a continuous stream of tasks.
Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration
romoreira/sfi2-energy-sustainability
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Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others.
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System
uic-jq/iopo
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Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems.
