Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey
Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcemen…
Deep Reinforcement LearningDistributed Computingreinforcement-learningReinforcement LearningFlexible Coded Distributed Convolution Computing for Enhanced Fault Tolerance and Numerical Stability in Distributed CNNs
Deploying Convolutional Neural Networks (CNNs) on resource-constrained devices necessitates efficient management of computational resources, often via distributed systems susceptible to latency from straggler nodes. This…
Computational EfficiencyDistributed ComputingManagementV2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario
This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative tra…
Distributed ComputingModel Predictive ControlTrajectory PlanningOptimization and Application of Cloud-based Deep Learning Architecture for Multi-Source Data Prediction
This study develops a cloud-based deep learning system for early prediction of diabetes, leveraging the distributed computing capabilities of the AWS cloud platform and deep learning technologies to achieve efficient and…
Deep LearningDistributed ComputingGPUManagement+2Asynchronous Stochastic Gradient Descent with Decoupled Backpropagation and Layer-Wise Updates
The increasing size of deep learning models has made distributed training across multiple devices essential. However, current methods such as distributed data-parallel training suffer from large communication and synchro…
Distributed ComputingOver-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint
Wireless networks supporting artificial intelligence have gained significant attention, with Over-the-Air Federated Learning emerging as a key application due to its unique transmission and distributed computing characte…
Distributed ComputingFederated LearningDeep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer
This book explores the role of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in driving the progress of big data analytics and management. The book focuses on simplifying the complex mathema…
Autonomous DrivingDeep LearningDistributed ComputingManagementAccelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
Modern realities and trends in learning require more and more generalization ability of models, which leads to an increase in both models and training sample size. It is already difficult to solve such tasks in a single …
Distributed ComputingFederated LearningUsing Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning
In distributed computing environments, collaborative machine learning enables multiple clients to train a global model collaboratively. To preserve privacy in such settings, a common technique is to utilize frequent upda…
Bilevel OptimizationDistributed ComputingFairnessFederated LearningStability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems
We examine stability properties of primal-dual gradient flow dynamics for composite convex optimization problems with multiple, possibly nonsmooth, terms in the objective function under the generalized consensus constrai…
Distributed ComputingDistributed quasi-Newton robust estimation under differential privacy
For distributed computing with Byzantine machines under Privacy Protection (PP) constraints, this paper develops a robust PP distributed quasi-Newton estimation, which only requires the node machines to transmit five vec…
Distributed ComputingHeterogeneity: An Open Challenge for Federated On-board Machine Learning
The design of satellite missions is currently undergoing a paradigm shift from the historical approach of individualised monolithic satellites towards distributed mission configurations, consisting of multiple small sate…
Distributed ComputingEdge-computingFederated LearningResidual-INR: Communication Efficient On-Device Learning Using Implicit Neural Representation
Edge computing is a distributed computing paradigm that collects and processes data at or near the source of data generation. The on-device learning at edge relies on device-to-device wireless communication to facilitate…
CPUDistributed ComputingEdge-computingBinary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection
In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as non-negative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the nu…
Dimensionality ReductionDistributed ComputingModel SelectionDistributed Memory Approximate Message Passing
Approximate message passing (AMP) algorithms are iterative methods for signal recovery in noisy linear systems. In some scenarios, AMP algorithms need to operate within a distributed network. To address this challenge, t…
Distributed ComputingOptimization of breeding program design through stochastic simulation with evolutionary algorithms
The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing p…
Distributed ComputingEvolutionary AlgorithmsregressionCorrelations Are Ruining Your Gradient Descent
Herein the topics of (natural) gradient descent, data decorrelation, and approximate methods for backpropagation are brought into a common discussion. Natural gradient descent illuminates how gradient vectors, pointing a…
Distributed ComputingDistributed Semantic Segmentation with Efficient Joint Source and Task Decoding
Distributed computing in the context of deep neural networks (DNNs) implies the execution of one part of the network on edge devices and the other part typically on a large-scale cloud platform. Conventional methods prop…
DecoderDistributed ComputingSemantic SegmentationDistributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine
High-performance computing (HPC) has revolutionized our ability to perform detailed simulations of complex real-world processes. A prominent contemporary example is from aerospace propulsion, where HPC is used for rotati…
Distributed ComputingDeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset
High data rate and low-latency vehicle-to-vehicle (V2V) communication are essential for future intelligent transport systems to enable coordination, enhance safety, and support distributed computing and intelligence requ…
Distributed Computing