Edge-computing
159 papers with code • 0 benchmarks • 0 datasets
Deep Learning on EDGE devices
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Latest papers
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
jyzgh/fedlps
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FedLPS leverages principles from transfer learning to facilitate the deployment of multiple tasks on a single device by dividing the local model into a shareable encoder and task-specific encoders.
Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image Enhancement
Edge computing enables rapid data processing and decision-making on satellite payloads.
Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
cpngroup/wptmec
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Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.
WidthFormer: Toward Efficient Transformer-based BEV View Transformation
chenhongyiyang/widthformer
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In this work, we present WidthFormer, a novel transformer-based Bird's-Eye-View (BEV) 3D detection method tailored for real-time autonomous-driving applications.
An effective and efficient green federated learning method for one-layer neural networks
Federated learning (FL) is one of the most active research lines in machine learning, as it allows the training of collaborative models in a distributed way, an interesting option in many real-world environments, such as the Internet of Things, allowing the use of these models in edge computing devices.
Control Aspects for Using RIS in Latency-Constrained Mobile Edge Computing
This paper investigates the role and the impact of control operations for dynamic mobile edge computing (MEC) empowered by Reconfigurable Intelligent Surfaces (RISs), in which multiple devices offload their computation tasks to an access point (AP) equipped with an edge server (ES), with the help of the RIS.
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.
