Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures
Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data…
Distributed ComputingManagementCentroid Approximation for Byzantine-Tolerant Federated Learning
Federated learning allows each client to keep its data locally when training machine learning models in a distributed setting. Significant recent research established the requirements that the input must satisfy in order…
Distributed ComputingFederated LearningAccessibility Barriers in Multi-Terabyte Public Datasets: The Gap Between Promise and Practice
The promise of "free and open" multi-terabyte datasets often collides with harsh realities. While these datasets may be technically accessible, practical barriers -- from processing complexity to hidden costs -- create a…
Distributed ComputingBanditWare: A Contextual Bandit-based Framework for Hardware Prediction
Distributed computing systems are essential for meeting the demands of modern applications, yet transitioning from single-system to distributed environments presents significant challenges. Misallocating resources in sha…
Distributed ComputingCompositional and Equilibrium-Free Conditions for Power System Stability -- Part II: Method and Application
This two-part paper proposes a compositional and equilibrium-free approach to analyzing power system stability. In Part I, we have established the stability theory and proposed stability conditions based on the delta dis…
Distributed ComputingDistributed Beamforming Using Decentralized Time Synchronization in a Six-Element Array
We demonstrate a distributed beamforming and beamsteering from a six-node distributed phased array using fully wireless coordination with decentralized time synchronization. In wireless applications such as distributed b…
Distributed ComputingBridging Quantized Artificial Neural Networks and Neuromorphic Hardware
Neuromorphic hardware has been proposed and also been produced for decades. One of the main goals of this hardware is to leverage distributed computing and event-driven circuit design and achieve power-efficient AI syste…
Distributed ComputingEdge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, …
Autonomous DrivingBenchmarkingDistributed ComputingLow-latency processing+3SacFL: Self-Adaptive Federated Continual Learning for Resource-Constrained End Devices
The proliferation of end devices has led to a distributed computing paradigm, wherein on-device machine learning models continuously process diverse data generated by these devices. The dynamic nature of this data, chara…
Continual LearningContrastive LearningDistributed ComputingEnhancing Variable Selection in Large-scale Logistic Regression: Leveraging Manual Labeling with Beneficial Noise
In large-scale supervised learning, penalized logistic regression (PLR) effectively addresses the overfitting problem by introducing regularization terms yet its performance still depends on efficient variable selection …
Distributed ComputingVariable SelectionYou Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models
Fine-tuning plays a crucial role in adapting models to downstream tasks with minimal training efforts. However, the rapidly increasing size of foundation models poses a daunting challenge for accommodating foundation mod…
AllDistributed Computingparameter-efficient fine-tuningTree-Guided $L_1$-Convex Clustering
Convex clustering is a modern clustering framework that guarantees globally optimal solutions and performs comparably to other advanced clustering methods. However, obtaining a complete dendrogram (clusterpath) for large…
ClusteringComputational EfficiencyDistributed ComputingDistributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions
Language models (LMs) are machine learning models designed to predict linguistic patterns by estimating the probability of word sequences based on large-scale datasets, such as text. LMs have a wide range of applications…
2D Object DetectionDistributed ComputingLarge Language ModelMachine Translation+2Goal-oriented Spectrum Sharing: Trading Edge Inference Power for Data Streaming Performance
We study the problem of spectrum sharing between goal-oriented (GO) and legacy data-oriented (DO) systems. For the former, data quality and representation is no longer optimized based on classical communication key perfo…
Distributed ComputingFundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association
Secure aggregation is motivated by federated learning (FL) where a cloud server aims to compute an averaged model (i.e., weights of deep neural networks) of the locally-trained models of numerous clients, while adhering …
Distributed ComputingFederated LearningBenchmarking Dynamic SLO Compliance in Distributed Computing Continuum Systems
Ensuring Service Level Objectives (SLOs) in large-scale architectures, such as Distributed Computing Continuum Systems (DCCS), is challenging due to their heterogeneous nature and varying service requirements across diff…
BenchmarkingCPUDistributed ComputingSupporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform
Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly …
Distributed ComputingDiversityGPUA Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments
Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data. With the increasing deployment of machine learning model…
Autonomous VehiclesDistributed ComputingFederated LearningModel extraction+1Evolutionary Algorithms Approach For Search Based On Semantic Document Similarity
Advancements in cloud computing and distributed computing have fostered research activities in Computer science. As a result, researchers have made significant progress in Neural Networks, Evolutionary Computing Algorith…
Cloud ComputingDistributed ComputingEvolutionary AlgorithmsSemantic Similarity+5Continuous K-Max Bandits
We study the $K$-Max combinatorial multi-armed bandits problem with continuous outcome distributions and weak value-index feedback: each base arm has an unknown continuous outcome distribution, and in each round the lear…
Distributed ComputingMulti-Armed BanditsRecommendation SystemsScheduling