Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Randomized Polar Codes for Anytime Distributed Machine Learning
We present a novel distributed computing framework that is robust to slow compute nodes, and is capable of both approximate and exact computation of linear operations. The proposed mechanism integrates the concepts of ra…
Cloud ComputingDistributed ComputingRevolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning
Deep learning-based disease diagnosis applications are essential for accurate diagnosis at various disease stages. However, using personal data exposes traditional centralized learning systems to privacy concerns. On the…
Distributed ComputingFederated LearningPneumonia DetectionPrivacy Preserving+1Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction
Despite the recent success of Graph Neural Networks (GNNs), it remains challenging to train GNNs on large-scale graphs due to neighbor explosions. As a remedy, distributed computing becomes a promising solution by levera…
Distributed ComputingGPUAdvancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery
During neurosurgery, medical images of the brain are used to locate tumors and critical structures, but brain tissue shifts make pre-operative images unreliable for accurate removal of tumors. Intra-operative imaging can…
Distributed ComputingImproving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks
Advanced Air Mobility (AAM) introduces a new, efficient mode of transportation with the use of vehicle autonomy and electrified aircraft to provide increasingly autonomous transportation between previously underserved ma…
Distributed Computingreinforcement-learningIterative Sketching for Secure Coded Regression
Linear regression is a fundamental and primitive problem in supervised machine learning, with applications ranging from epidemiology to finance. In this work, we propose methods for speeding up distributed linear regress…
Distributed ComputingEpidemiologyregressionScaling Data Science Solutions with Semantics and Machine Learning: Bosch Case
Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such …
Data IntegrationDistributed ComputingDistributed bundle adjustment with block-based sparse matrix compression for super large scale datasets
We propose a distributed bundle adjustment (DBA) method using the exact Levenberg-Marquardt (LM) algorithm for super large-scale datasets. Most of the existing methods partition the global map to small ones and conduct b…
Distributed ComputingOn Collaboration in Distributed Parameter Estimation with Resource Constraints
Effective resource allocation in sensor networks, IoT systems, and distributed computing is essential for applications such as environmental monitoring, surveillance, and smart infrastructure. Sensors or agents must opti…
Distributed Computingparameter estimationTowards a Better Theoretical Understanding of Independent Subnetwork Training
Modern advancements in large-scale machine learning would be impossible without the paradigm of data-parallel distributed computing. Since distributed computing with large-scale models imparts excessive pressure on commu…
Distributed ComputingGraph Convolution Based Efficient Re-Ranking for Visual Retrieval
Visual retrieval tasks such as image retrieval and person re-identification (Re-ID) aim at effectively and thoroughly searching images with similar content or the same identity. After obtaining retrieved examples, re-ran…
Distributed ComputingImage RetrievalPerson Re-IdentificationRe-Ranking+1Quasi-Newton Updating for Large-Scale Distributed Learning
Distributed computing is critically important for modern statistical analysis. Herein, we develop a distributed quasi-Newton (DQN) framework with excellent statistical, computation, and communication efficiency. In the D…
Distributed ComputingArchitectural 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+1Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced signif…
ArticlesDistributed ComputingFederated LearningPrivacy PreservingA Survey on Distributed Evolutionary Computation
The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC o…
Distributed ComputingDistributed OptimizationSurveyCooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer black-box optimiz…
CPUDistributed ComputingImplementing Dynamic Programming in Computability Logic Web
We present a novel definition of an algorithm and its corresponding algorithm language called CoLweb. The merit of CoLweb [1] is that it makes algorithm design so versatile. That is, it forces us to a high-level, proof-c…
Distributed ComputingA Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing
Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables effic…
Distributed ComputingFederated LearningComputationally Efficient Labeling of Cancer Related Forum Posts by Non-Clinical Text Information Retrieval
An abundance of information about cancer exists online, but categorizing and extracting useful information from it is difficult. Almost all research within healthcare data processing is concerned with formal clinical dat…
ClusteringDistributed ComputingInformation RetrievalRetrieval+1When Evolutionary Computation Meets Privacy
Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like distributed EC and surrogate-assisted EC. T…
Distributed ComputingDistributed OptimizationPrivacy Preserving