Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler Mitigation
Most multi-agent reinforcement learning (MARL) methods are limited in the scale of problems they can handle. With increasing numbers of agents, the number of training iterations required to find the optimal behaviors inc…
Distributed ComputingMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1FLHub: a Federated Learning model sharing service
As easy-to-use deep learning libraries such as Tensorflow and Pytorch are popular, it has become convenient to develop machine learning models. Due to privacy issues with centralized machine learning, recently, federated…
BIG-bench Machine LearningDistributed ComputingFederated LearningmodelRandom Alloy Codes and the Fundamental Limits of Coded Distributed Tensors
Tensors are a fundamental operation in distributed computing, \emph{e.g.,} machine learning, that are commonly distributed into multiple parallel tasks for large datasets. Stragglers and other failures can severely impac…
Distributed ComputingLBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to user is a common strategy used by online platforms to increase user engagement and platform revenue. Despite its proven effe…
Distributed ComputingMarketingSurrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models
Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients; for instance, geoscientific landscape evolution models. However, such models are at t…
Distributed ComputingEvolutionary AlgorithmsRay Based Distributed Autonomous Vehicle Research Platform
My project tackles the question of whether Ray can be used to quickly train autonomous vehicles using a simulator (Carla), and whether a platform robust enough for further research purposes can be built around it. Ray is…
Autonomous VehiclesDistributed ComputingImitation LearningLayerwise Geo-Distributed Computing between Cloud and IoT
In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed…
Distributed ComputingMPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning
In the modern era of computing, machine learning tools have demonstrated their potential in vital sectors, such as healthcare and finance, to derive proper inferences. The sensitive and confidential nature of the data in…
BenchmarkingBIG-bench Machine LearningDistributed ComputingPrivacy Preserving+1MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems
Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements. Recently proposed scheduling methods leverag…
Cloud ComputingDistributed ComputingEdge-computingSchedulingFleetX
Distributed training has become a pervasive and effective approach for training a large neural network (NN) model with processing massive data. However, it is very challenging to satisfy requirements from various NN mo…
CPUDistributed ComputingGPULanguage Modelling+2FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization
One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources…
Distributed ComputingFederated LearningLow-rank compressionMatrix Factorization / Decomposition+1Universal Swarm Computing by Nanorobots
Realization of universal computing units for nanorobots is highly promising in creating new and wide arrays of applications, particularly in the realm of distributed computation. However, such realization is also a chall…
Distributed ComputingHeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments
Deep neural networks (DNNs) exploit many layers and a large number of parameters to achieve excellent performance. The training process of DNN models generally handles large-scale input data with many sparse features, wh…
Distributed Computingreinforcement-learningReinforcement Learning (RL)SchedulingInteroperable and scalable echosounder data processing with Echopype
Echosounders are high-frequency sonar systems used to sense fish and zooplankton underwater. Their deployment on a variety of ocean observing platforms is generating vast amounts of data at an unprecedented speed from th…
Cloud ComputingDistributed ComputingGCNScheduler: Scheduling Distributed Computing Applications using Graph Convolutional Networks
We consider the classical problem of scheduling task graphs corresponding to complex applications on distributed computing systems. A number of heuristics have been previously proposed to optimize task scheduling with re…
Distributed ComputingSchedulingDistributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transport and robust optimization. With increa…
Distributed ComputingFederated LearningHigh Probability Generalization Bounds for Minimax Problems with Fast Rates
Minimax problems are receiving an increasing amount of attention in a wide range of applications in machine learning (ML), for instance, reinforcement learning, robust optimization, adversarial learning, and distributed …
Distributed ComputingGeneralization BoundsVocal Bursts Intensity PredictionDivide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations
One of the greatest challenges of reinforcement learning is efficient exploration, especially when training signals are sparse or deceptive. The main difficulty of exploration lies in the size and complexity of the state…
Distributed ComputingEfficient ExplorationCommunication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis
Classic and deep generalized canonical correlation analysis (GCCA) algorithms seek low-dimensional common representations of data entities from multiple ``views'' (e.g., audio and image) using linear transformations and …
Distributed ComputingDistributed OptimizationFederated LearningQuantizationCoded Computation across Shared Heterogeneous Workers with Communication Delay
Distributed computing enables large-scale computation tasks to be processed over multiple workers in parallel. However, the randomness of communication and computation delays across workers causes the straggler effect, w…
Distributed Computing