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Edge-computing

Deep Learning on EDGE devices

Papers

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Showing 601–625 of 1279 papers

TitleStatusHype
Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling—0
Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge ComputingCode1
FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge ComputingCode1
Energy-Efficient Blockchain-enabled User-Centric Mobile Edge ComputingCode1
FederatedTrust: A Solution for Trustworthy Federated Learning—0
A Pervasive Framework for Human Detection and Tracking—0
ViTA: A Vision Transformer Inference Accelerator for Edge Applications—0
Virtualization of Tiny Embedded Systems with a robust real-time capable and extensible Stack Virtual Machine REXAVM supporting Material-integrated Intelligent Systems and Tiny Machine Learning—0
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques—0
EdgeYOLO: An Edge-Real-Time Object DetectorCode2
Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation—0
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation—0
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs—0
EnergyShield: Provably-Safe Offloading of Neural Network Controllers for Energy Efficiency—0
Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach—0
On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence—0
Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading—0
Intelligent Proactive Fault Tolerance at the Edge through Resource Usage Prediction—0
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey—0
Hyperspectral Image Classification Using Deep Matrix CapsulesCode1
LesionAid: Vision Transformers-based Skin Lesion Generation and Classification—0
Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning—0
Energy-Efficient Vehicular Edge Computing with One-by-one Access Scheme—0
V2N Service Scaling with Deep Reinforcement Learning—0
SMDP-Based Dynamic Batching for Efficient Inference on GPU-Based Platforms—0
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