Papers Collaborative Inference
“Collaborative Inference” 태그가 달린 논문 68편 · 필터 해제
DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference
Due to limited resources on edge and different characteristics of deep neural network (DNN) models, it is a big challenge to optimize DNN inference performance in terms of energy consumption and end-to-end latency on edg…
Collaborative InferenceCPUDeep Reinforcement LearningGPUPrivaScissors: Enhance the Privacy of Collaborative Inference through the Lens of Mutual Information
Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus preserving privacy. Nevertheless, prior research …
Collaborative InferenceArchitectural Vision for Quantum Computing in the Edge-Cloud Continuum
Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge com…
Collaborative InferenceDistributed ComputingEdge-computingImage Classification+1Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms
The deployment flexibility and maneuverability of Unmanned Aerial Vehicles (UAVs) increased their adoption in various applications, such as wildfire tracking, border monitoring, etc. In many critical applications, UAVs c…
Collaborative InferenceDeep Reinforcement LearningMLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference
The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices. A typical dilemma is: in order to provide complex in…
Collaborative InferenceDeep Reinforcement LearningGPUMulti-Task Learning+1Petals: Collaborative Inference and Fine-tuning of Large Models
Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can download pretrained models of this scale. Still, …
Collaborative InferenceCollaborative Inference for AI-Empowered IoT Devices
Artificial intelligence (AI) technologies, and particularly deep learning systems, are traditionally the domain of large-scale cloud servers, which have access to high computational and energy resources. Nonetheless, in …
Collaborative InferenceDuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain
With the wide application of face recognition systems, there is rising concern that original face images could be exposed to malicious intents and consequently cause personal privacy breaches. This paper presents DuetFac…
Collaborative InferenceFace RecognitionPrivacy PreservingVirtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks
Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tis…
Collaborative InferenceVirtual StainingTowards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification
Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. RFFI is implemented in the wireless…
Collaborative InferenceFault-Tolerant Collaborative Inference through the Edge-PRUNE Framework
Collaborative inference has received significant research interest in machine learning as a vehicle for distributing computation load, reducing latency, as well as addressing privacy preservation in communications. Recen…
BIG-bench Machine LearningCollaborative InferenceDistributed ComputingDecentralized Low-Latency Collaborative Inference via Ensembles on the Edge
The success of deep neural networks (DNNs) is heavily dependent on computational resources. While DNNs are often employed on cloud servers, there is a growing need to operate DNNs on edge devices. Edge devices are typica…
Collaborative InferenceMulti-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning
Recently, deploying deep neural network (DNN) models via collaborative inference, which splits a pre-trained model into two parts and executes them on user equipment (UE) and edge server respectively, becomes attractive.…
Collaborative InferenceCPUFeature CompressionC-NMT: A Collaborative Inference Framework for Neural Machine Translation
Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices. Albeit beneficial for other tasks, CI has never been applied to …
Collaborative InferenceMachine TranslationNMTTranslationAn Approach to Inference-Driven Dialogue Management within a Social Chatbot
We present a chatbot implementing a novel dialogue management approach based on logical inference. Instead of framing conversation a sequence of response generation tasks, we model conversation as a collaborative inferen…
ChatbotCollaborative InferenceDialogue ManagementGraph Matching+2SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
Self-supervised learning has been shown to be very effective in learning useful representations, and yet much of the success is achieved in data types such as images, audio, and text. The success is mainly enabled by tak…
Collaborative InferenceContrastive LearningRepresentation LearningSelf-Supervised LearningSecurity Analysis of Capsule Network Inference using Horizontal Collaboration
The traditional convolution neural networks (CNN) have several drawbacks like the Picasso effect and the loss of information by the pooling layer. The Capsule network (CapsNet) was proposed to address these challenges be…
Collaborative InferenceSelf-Driving CarsEARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference
Collaborative inference enables resource-constrained edge devices to make inferences by uploading inputs (e.g., images) to a server (i.e., cloud) where the heavy deep learning models run. While this setup works cost-effe…
Collaborative InferenceOut-of-Distribution DetectionOut of Distribution (OOD) DetectionCommunication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation
Split-learning (SL) has recently gained popularity due to its inherent privacy-preserving capabilities and ability to enable collaborative inference for devices with limited computational power. Standard SL algorithms as…
Collaborative InferencePrivacy PreservingEnergy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels
Today's intelligent applications can achieve high performance accuracy using machine learning (ML) techniques, such as deep neural networks (DNNs). Traditionally, in a remote DNN inference problem, an edge device transmi…
Collaborative Inferenceimage-classificationImage ClassificationModel Compression