Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
CollaFuse: Collaborative Diffusion Models
In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffusion models poses numerous challenges, p…
Edge-computingFederated LearningImage GenerationTowards Audio Codec-based Speech Separation
Recent improvements in neural audio codec (NAC) models have generated interest in adopting pre-trained codecs for a variety of speech processing applications to take advantage of the efficiencies gained from high compres…
Edge-computingSpeech SeparationReconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices. However, obstacles such as buildings ma…
Deep Reinforcement LearningEdge-computingMulti-agent Reinforcement LearningQoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing: An Online Joint Optimization Approach
In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (L…
Edge-computingTrajectory PlanningDesign and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devices
Edge computing has recently emerged as a promising paradigm to boost the performance of distributed learning by leveraging the distributed resources at edge nodes. Architecturally, the introduction of edge nodes adds an …
Edge-computingFair Computation Offloading for RSMA-Assisted Mobile Edge Computing Networks
Rate splitting multiple access (RSMA) provides a flexible transmission framework that can be applied in mobile edge computing (MEC) systems. However, the research work on RSMA-assisted MEC systems is still at the infancy…
Edge-computingFairnessHypergraph MatchingHeterogeneous Federated Learning with Convolutional and Spiking Neural Networks
Federated learning (FL) has emerged as a promising paradigm for training models on decentralized data while safeguarding data privacy. Most existing FL systems, however, assume that all machine learning models are of the…
DiversityEdge-computingFederated LearningPrivacy PreservingED-sKWS: Early-Decision Spiking Neural Networks for Rapid,and Energy-Efficient Keyword Spotting
Keyword Spotting (KWS) is essential in edge computing requiring rapid and energy-efficient responses. Spiking Neural Networks (SNNs) are well-suited for KWS for their efficiency and temporal capacity for speech. To furth…
Edge-computingKeyword SpottingLLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices
Limited by the scale and diversity of time series data, the neural networks trained on time series data often overfit and show unsatisfacotry performances. In comparison, large language models (LLMs) recently exhibit imp…
Edge-computingTime SeriesWorld KnowledgeDNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network (DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often computation-intensive, requiring substantial comput…
Deep Reinforcement LearningEdge-computingACCO: Automated Causal CNN Scheduling Optimizer for Real-Time Edge Accelerators
Spatio-Temporal Convolutional Neural Networks (ST-CNN) allow extending CNN capabilities from image processing to consecutive temporal-pattern recognition. Generally, state-of-the-art (SotA) ST-CNNs inflate the feature ma…
Edge-computingSchedulingMixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation
Over-the-Air Federated Learning (OTA-FL) is a privacy-preserving distributed learning mechanism, by aggregating updates in the electromagnetic channel rather than at the server. A critical research gap in existing OTA-FL…
Computational EfficiencyEdge-computingFederated LearningPrivacy Preserving+1Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach
Optimizing the deployment of large language models (LLMs) in edge computing environments is critical for enhancing privacy and computational efficiency. Toward efficient wireless LLM inference in edge computing, this stu…
Computational EfficiencyEdge-computingModel-based Reinforcement LearningData Quality in Edge Machine Learning: A State-of-the-Art Survey
Data-driven Artificial Intelligence (AI) systems trained using Machine Learning (ML) are shaping an ever-increasing (in size and importance) portion of our lives, including, but not limited to, recommendation systems, au…
Autonomous DrivingEdge-computingMarketingRecommendation SystemsAdaptive and Parallel Split Federated Learning in Vehicular Edge Computing
Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent transportation systems by accommodating artificial intelligence (AI) at the vehicular edge computing (VEC) system. Federated learn…
Edge-computingFederated LearningEfficient Model Compression for Hierarchical Federated Learning
Federated learning (FL), as an emerging collaborative learning paradigm, has garnered significant attention due to its capacity to preserve privacy within distributed learning systems. In these systems, clients collabora…
Edge-computingFederated LearningmodelModel CompressionAthena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information
Large Language Models (LLMs) have significantly advanced natural language processing tasks such as machine translation, text generation, and sentiment analysis. However, their large size, often consisting of billions of …
Edge-computingMachine TranslationQuantizationSentiment Analysis+1A motion-based compression algorithm for resource-constrained video camera traps
Field-captured video facilitates detailed studies of spatio-temporal aspects of animal locomotion, decision-making and environmental interactions including predator-prey relationships and habitat utilisation. But even th…
Decision MakingEdge-computingVideo CompressionOn Hardware-efficient Inference in Probabilistic Circuits
Probabilistic circuits (PCs) offer a promising avenue to perform embedded reasoning under uncertainty. They support efficient and exact computation of various probabilistic inference tasks by design. Hence, hardware-effi…
Edge-computingEfficient Multimodal Large Language Models: A Survey
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning. However, the extensive model size and hi…
Edge-computingQuestion AnsweringSurveyVisual Question Answering