Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing
In the traditional vehicular network, computing tasks generated by the vehicles are usually uploaded to the cloud for processing. However, since task offloading toward the cloud will cause a large delay, vehicular edge c…
Deep Reinforcement LearningEdge-computingFederated LearningA Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions
Recent technological advancements have considerately improved healthcare systems to provide various intelligent healthcare services and improve the quality of life. Federated learning (FL), a new branch of artificial int…
Drug DiscoveryEdge-computingFederated LearningManagement+1Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. Yet, with the growing complexity of SNN algorithms, it isn't easy to properly benchmar…
BenchmarkingEdge-computingEfficient Parallel Split Learning over Resource-constrained Wireless Edge Networks
The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, w…
Edge-computingFederated LearningManagementEdge-Based Video Analytics: A Survey
Edge computing has been getting a momentum with ever-increasing data at the edge of the network. In particular, huge amounts of video data and their real-time processing requirements have been increasingly hindering the …
Cloud ComputingEdge-computingManagementSurveyTinyML: Tools, Applications, Challenges, and Future Research Directions
In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the…
Edge-computingFederated Learning for Metaverse: A Survey
The metaverse, which is at the stage of innovation and exploration, faces the dilemma of data collection and the problem of private data leakage in the process of development. This can seriously hinder the widespread dep…
Edge-computingFederated LearningPrivacy PreservingSurveyHardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms
Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model designs mainly focus on task accuracy, lac…
Edge-computingGPUGraph Neural NetworkNeural Architecture SearchHierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. How…
Edge-computingFederated LearningPersonalized Federated LearningSchedulingChannel-Aware Distillation Transformer for Depth Estimation on Nano Drones
Autonomous navigation of drones using computer vision has achieved promising performance. Nano-sized drones based on edge computing platforms are lightweight, flexible, and cheap, thus suitable for exploring narrow space…
Autonomous NavigationDepth EstimationEdge-computingGPU+1Crowd Counting with Online Knowledge Learning
Efficient crowd counting models are urgently required for the applications in scenarios with limited computing resources, such as edge computing and mobile devices. A straightforward method to achieve this is knowledge d…
Crowd CountingEdge-computingKnowledge DistillationEarCough: Enabling Continuous Subject Cough Event Detection on Hearables
Cough monitoring can enable new individual pulmonary health applications. Subject cough event detection is the foundation for continuous cough monitoring. Recently, the rapid growth in smart hearables has opened new oppo…
Edge-computingEvent DetectionTowards Commonsense Knowledge based Fuzzy Systems for Supporting Size-Related Fine-Grained Object Detection
Deep learning has become the dominating approach for object detection. To achieve accurate fine-grained detection, one needs to employ a large enough model and a vast amount of data annotations. In this paper, we propose…
Edge-computingobject-detectionObject DetectionArtificial Intelligence based drone for early disease detection and precision pesticide management in cashew farming
The use of unmanned aerial vehicles (UAV) is revolutionizing the agricultural industry. Cashews are grown by approximately 70% of small and marginal farmers, and the cashew industry plays a critical role in their economi…
Edge-computingManagementRobust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things
Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computa…
Edge-computingDistributed Learning Meets 6G: A Communication and Computing Perspective
With the ever-improving computing capabilities and storage capacities of mobile devices in line with evolving telecommunication network paradigms, there has been an explosion of research interest towards exploring Distri…
Edge-computingFederated LearningMulti-agent Reinforcement LearningA Unified Momentum-based Paradigm of Decentralized SGD for Non-Convex Models and Heterogeneous Data
Emerging distributed applications recently boosted the development of decentralized machine learning, especially in IoT and edge computing fields. In real-world scenarios, the common problems of non-convexity and data he…
Edge-computingglobal-optimizationSemi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown promising for this application. This approach mo…
Demand ForecastingEdge-computingTiny Classifier Circuits: Evolving Accelerators for Tabular Data
A typical machine learning (ML) development cycle for edge computing is to maximise the performance during model training and then minimise the memory/area footprint of the trained model for deployment on edge devices ta…
Edge-computingEdge computing on TPU for brain implant signal analysis
The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are av…
Contrastive LearningDeep LearningEdge-computingSpike Sorting+1