Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Adaptive Synaptogenesis Implemented on a Nanomagnetic Platform
The human brain functions very differently from artificial neural networks (ANN) and possesses unique features that are absent in ANN. An important one among them is "adaptive synaptogenesis" that modifies synaptic weigh…
Edge-computingHippocampusLifelong learningUndermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders
EdgeIoT represents an approach that brings together mobile edge computing with Internet of Things (IoT) devices, allowing for data processing close to the data source. Sending source data to a server is bandwidth-intensi…
Edge-computingFederated LearningThe More is not the Merrier: Investigating the Effect of Client Size on Federated Learning
Federated Learning (FL) has been introduced as a way to keep data local to clients while training a shared machine learning model, as clients train on their local data and send trained models to a central aggregator. It …
Edge-computingFederated LearningJupiter: Fast and Resource-Efficient Collaborative Inference of Generative LLMs on Edge Devices
Generative large language models (LLMs) have garnered significant attention due to their exceptional capabilities in various AI tasks. Traditionally deployed in cloud datacenters, LLMs are now increasingly moving towards…
Collaborative InferenceEdge-computingExplainability and Continual Learning meet Federated Learning at the Network Edge
As edge devices become more capable and pervasive in wireless networks, there is growing interest in leveraging their collective compute power for distributed learning. However, optimizing learning at the network edge en…
Continual LearningEdge-computingExplainable ModelsFederated Learning+2MCP Bridge: A Lightweight, LLM-Agnostic RESTful Proxy for Model Context Protocol Servers
Large Language Models (LLMs) are increasingly augmented with external tools through standardized interfaces like the Model Context Protocol (MCP). However, current MCP implementations face critical limitations: they typi…
Edge-computingNeural Signal Compression using RAMAN tinyML Accelerator for BCI Applications
High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings often produce vast amounts of data that must be wirelessly transmitted for s…
Edge-computingLearning in Spiking Neural Networks with a Calcium-based Hebbian Rule for Spike-timing-dependent Plasticity
Understanding how biological neural networks are shaped via local plasticity mechanisms can lead to energy-efficient and self-adaptive information processing systems, which promises to mitigate some of the current roadbl…
Edge-computingFedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing
Federated learning is a distributed machine learning framework to collaboratively train a global model without uploading privacy-sensitive data onto a centralized server. Usually, this framework is applied to edge device…
Edge-computingFederated LearningOptimistic Learning for Communication Networks
AI/ML-based tools are at the forefront of resource management solutions for communication networks. Deep learning, in particular, is highly effective in facilitating fast and high-performing decision-making whenever repr…
Edge-computingManagementIndustrial Internet Robot Collaboration System and Edge Computing Optimization
In a complex environment, for a mobile robot to safely and collision - free avoid all obstacles, it poses high requirements for its intelligence level. Given that the information such as the position and geometric charac…
Edge-computingCooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks
Accurate and real-time three-dimensional (3D) pose estimation is challenging in resource-constrained and dynamic environments owing to its high computational complexity. To address this issue, this study proposes a novel…
3D Human Pose Estimation3D Pose EstimationEdge-computingPose EstimationEdge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML
Hornbills, an iconic species of Malaysia's biodiversity, face threats from habi-tat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging …
Edge-computingspeech-recognitionSpeech RecognitionEfficient and Sustainable Task Offloading in UAV-Assisted MEC Systems via Meta Deep Reinforcement Learning
Integrated into existing Mobile Edge Computing (MEC) systems, Unmanned Aerial Vehicles (UAVs) serve as a cornerstone in meeting the stringent requirements of future Internet of Things (IoT) networks. The current endeavor…
Deep Reinforcement LearningEdge-computingPolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification
Deep learning models have achieved significant success in various image related tasks. However, they often encounter challenges related to computational complexity and overfitting. In this paper, we propose an efficient …
Edge-computingimage-classificationImage ClassificationIntelligent IoT Attack Detection Design via ODLLM with Feature Ranking-based Knowledge Base
The widespread adoption of Internet of Things (IoT) devices has introduced significant cybersecurity challenges, particularly with the increasing frequency and sophistication of Distributed Denial of Service (DDoS) attac…
Edge-computingSmall Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications
Small object detection (SOD) is a critical yet challenging task in computer vision, with applications like spanning surveillance, autonomous systems, medical imaging, and remote sensing. Unlike larger objects, small obje…
ArticlesData AugmentationDefect DetectionDomain Adaptation+9UAV-Assisted MEC for Disaster Response: Stackelberg Game-Based Resource Optimization
The unmanned aerial vehicle assisted multi-access edge computing (UAV-MEC) technology has been widely applied in the sixth-generation era. However, due to the limitations of energy and computing resources in disaster are…
Disaster ResponseEdge-computingMulti-Agent Deep Reinforcement Learning for Safe Autonomous Driving with RICS-Assisted MEC
Environment sensing and fusion via onboard sensors are envisioned to be widely applied in future autonomous driving networks. This paper considers a vehicular system with multiple self-driving vehicles that is assisted b…
Autonomous DrivingDeep Reinforcement LearningEdge-computingFMDConv: Fast Multi-Attention Dynamic Convolution via Speed-Accuracy Trade-off
Spatial convolution is fundamental in constructing deep Convolutional Neural Networks (CNNs) for visual recognition. While dynamic convolution enhances model accuracy by adaptively combining static kernels, it incurs sig…
Edge-computing