Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Scalable and Hybrid Ensemble-Based Causality Discovery
Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience. To discover causal relationships, many data-…
BenchmarkingDistributed ComputingTime SeriesTime Series AnalysisAccelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that…
Distributed ComputingGravitational Wave DetectionEdge-assisted Democratized Learning Towards Federated Analytics
A recent take towards Federated Analytics (FA), which allows analytical insights of distributed datasets, reuses the Federated Learning (FL) infrastructure to evaluate the summary of model performances across the trainin…
Distributed ComputingEdge-computingFederated LearningPrivacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management
Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, artificial intelligence (AI) model training, and wireless communications. However, due to the privacy c…
Distributed ComputingFederated LearningManagementPrivacy Preserving+1Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) app…
DecoderDistributed ComputingAn Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged…
Distributed ComputingFairnessFederated LearningResource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism
We study exploration in stochastic multi-armed bandits when we have access to a divisible resource that can be allocated in varying amounts to arm pulls. We focus in particular on the allocation of distributed computing …
Distributed ComputingMulti-Armed BanditsA Closer Look at Codistillation for Distributed Training
Codistillation has been proposed as a mechanism to share knowledge among concurrently trained models by encouraging them to represent the same function through an auxiliary loss. This contrasts with the more commonly use…
Distributed ComputingOn Artificial Life and Emergent Computation in Physical Substrates
In living systems, we often see the emergence of the ingredients necessary for computation -- the capacity for information transmission, storage, and modification -- begging the question of how we may exploit or imitate …
Artificial LifeDistributed ComputingLarge-scale Neural Solvers for Partial Differential Equations
Solving partial differential equations (PDE) is an indispensable part of many branches of science as many processes can be modelled in terms of PDEs. However, recent numerical solvers require manual discretization of the…
Distributed ComputingCommunication-efficient distributed eigenspace estimation
Distributed computing is a standard way to scale up machine learning and data science algorithms to process large amounts of data. In such settings, avoiding communication amongst machines is paramount for achieving high…
Distributed ComputingAnalog Lagrange Coded Computing
A distributed computing scenario is considered, where the computational power of a set of worker nodes is used to perform a certain computation task over a dataset that is dispersed among the workers. Lagrange coded comp…
Distributed ComputingFedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair al…
BIG-bench Machine LearningDistributed ComputingFederated LearningEvaluation of Federated Learning in Phishing Email Detection
The use of Artificial Intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which opens it up to a myriad of privacy, trust, and legal issues. Moreover, organizations are…
Distributed ComputingFederated LearningPrivacy PreservingTransfer LearningMinimum Overhead Beamforming and Resource Allocation in D2D Edge Networks
Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. A key challenge in providing this capability is the requirement for judicious management of t…
CPUDistributed ComputingManagementDeep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) app…
DecoderDistributed ComputingSimplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective
In recent years, substantial progress has been made on Graph Convolutional Networks (GCNs). However, the computing of GCN usually requires a large memory space for keeping the entire graph. In consequence, GCN is not fle…
Community DetectionDistributed ComputingGraph EmbeddingNode ClassificationResource Aware Multifidelity Active Learning for Efficient Optimization
Traditional methods for black box optimization require a considerable number of evaluations which can be time consuming, unpractical, and often unfeasible for many engineering applications that rely on accurate represent…
Active LearningBayesian OptimizationDistributed ComputingRobust Multi-Agent Multi-Armed Bandits
Recent works have shown that agents facing independent instances of a stochastic $K$-armed bandit can collaborate to decrease regret. However, these works assume that each agent always recommends their individual best-ar…
Distributed ComputingMulti-Armed BanditsRecommendation SystemsCoded Distributed Computing with Partial Recovery
Coded computation techniques provide robustness against straggling workers in distributed computing. However, most of the existing schemes require exact provisioning of the straggling behaviour and ignore the computation…
Distributed Computing