Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which are the cornerstone of urgent analytics…
Distributed ComputingEdge-computingQuantum Machine LearningGaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems
Efficient inference in high-dimensional models is a central challenge in machine learning. We introduce the Gaussian Ensemble Belief Propagation (GEnBP) algorithm, which combines the strengths of the Ensemble Kalman Filt…
Computational EfficiencyDistributed ComputingA Generalization of Arrow's Impossibility Theorem Through Combinatorial Topology
To the best of our knowledge, a complete characterization of the domains that escape the famous Arrow's impossibility theorem remains an open question. We believe that different ways of proving Arrovian theorems illumina…
Distributed ComputingProgramming Distributed Collective Processes in the eXchange Calculus
Recent trends like the Internet of Things (IoT) suggest a vision of dense and multi-scale deployments of computing devices in nearly all kinds of environments. A prominent engineering challenge revolves around programmin…
Distributed ComputingDistributed Solvers for Network Linear Equations with Scalarized Compression
Distributed computing is fundamental to multi-agent systems, with solving distributed linear equations as a typical example. In this paper, we study distributed solvers for network linear equations over a network with no…
Distributed ComputingFederated Analytics for 6G Networks: Applications, Challenges, and Opportunities
Extensive research is underway to meet the hyper-connectivity demands of 6G networks, driven by applications like XR/VR and holographic communications, which generate substantial data requiring network-based processing, …
Distributed ComputingFederated LearningBalanced Multi-modal Federated Learning via Cross-Modal Infiltration
Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data. Current FL paradigms primarily focus on uni-modal…
Distributed ComputingFederated LearningPrivacy PreservingTransfer LearningScalable Agent-Based Modeling for Complex Financial Market Simulations
In this study, we developed a computational framework for simulating large-scale agent-based financial markets. Our platform supports trading multiple simultaneous assets and leverages distributed computing to scale the …
Decision MakingDistributed ComputingPrivacy-preserving quantum federated learning via gradient hiding
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limi…
Distributed ComputingFederated LearningIncremental LearningPrivacy Preserving+1FlexModel: A Framework for Interpretability of Distributed Large Language Models
With the growth of large language models, now incorporating billions of parameters, the hardware prerequisites for their training and deployment have seen a corresponding increase. Although existing tools facilitate mode…
Distributed ComputingGPUThe Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning
With the advance of the powerful heterogeneous, parallel and distributed computing systems and ever increasing immense amount of data, machine learning has become an indispensable part of cutting-edge technology, scienti…
Deep LearningDistributed ComputingFederated LearningLearning-driven Zero Trust in Distributed Computing Continuum Systems
Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in Distributed Computing Continuum Systems (DCCS). Implementing centralized ZT architecture is seen as unsuitable …
Decision MakingDistributed ComputingManagementRepresentation LearningNon-Sequential Ensemble Kalman Filtering using Distributed Arrays
This work introduces a new, distributed implementation of the Ensemble Kalman Filter (EnKF) that allows for non-sequential assimilation of large datasets in high-dimensional problems. The traditional EnKF algorithm is co…
Distributed ComputingActive Inference on the Edge: A Design Study
Machine Learning (ML) is a common tool to interpret and predict the behavior of distributed computing systems, e.g., to optimize the task distribution between devices. As more and more data is created by Internet of Thin…
Distributed ComputingSimplicial Models for the Epistemic Logic of Faulty Agents
In recent years, several authors have been investigating simplicial models, a model of epistemic logic based on higher-dimensional structures called simplicial complexes. In the original formulation, simplicial models we…
Distributed ComputingFlood and Echo Net: Algorithmically Aligned GNNs that Generalize
Most Graph Neural Networks follow the standard message-passing framework where, in each step, all nodes simultaneously communicate with each other. We want to challenge this paradigm by aligning the computation more clos…
Distributed ComputingMAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems
Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs. Our analysis, grounded in real-world large mode…
Distributed ComputingGPULockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training
Federated learning (FL) is vulnerable to backdoor attacks due to its distributed computing nature. Existing defense solution usually requires larger amount of computation in either the training or testing phase, which l…
backdoor defenseDistributed ComputingFederated LearningLockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training
Federated learning (FL) is vulnerable to backdoor attacks due to its distributed computing nature. Existing defense solution usually requires larger amount of computation in either the training or testing phase, which l…
backdoor defenseDistributed ComputingFederated LearningData-Juicer: A One-Stop Data Processing System for Large Language Models
The immense evolution in Large Language Models (LLMs) has underscored the importance of massive, heterogeneous, and high-quality data. A data recipe is a mixture of data from different sources for training LLMs, which pl…
Distributed Computing