Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization
Bi-level optimization (BO) has become a fundamental mathematical framework for addressing hierarchical machine learning problems. As deep learning models continue to grow in size, the demand for scalable bi-level optimiz…
Computational EfficiencyDistributed ComputingMarking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems
Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) de…
Distributed ComputingPrivacy PreservingRecommendation SystemsCoded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
Coded computing has emerged as a promising framework for tackling significant challenges in large-scale distributed computing, including the presence of slow, faulty, or compromised servers. In this approach, each worker…
DecoderDistributed ComputingLearning TheoryLogical Characterizations of Recurrent Graph Neural Networks with Reals and Floats
In pioneering work from 2019, Barcel\'o and coauthors identified logics that precisely match the expressive power of constant iteration-depth graph neural networks (GNNs) relative to properties definable in first-order l…
Distributed ComputingSparse $L^1$-Autoencoders for Scientific Data Compression
Scientific datasets present unique challenges for machine learning-driven compression methods, including more stringent requirements on accuracy and mitigation of potential invalidating artifacts. Drawing on results from…
compressed sensingData CompressionDistributed ComputingPrivacy-aware Berrut Approximated Coded Computing for Federated Learning
Federated Learning (FL) is an interesting strategy that enables the collaborative training of an AI model among different data owners without revealing their private datasets. Even so, FL has some privacy vulnerabilities…
Distributed ComputingFederated LearningFedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data
Heterogeneity resulting from label distribution skew and data scarcity can lead to inaccuracy and unfairness in intelligent communication applications that mainly rely on distributed computing. To deal with it, this pape…
Contrastive LearningDistributed ComputingFairnessFederated Learning+3Trust Dynamics and Market Behavior in Cryptocurrency: A Comparative Study of Centralized and Decentralized Exchanges
In the rapidly evolving cryptocurrency landscape, trust is a critical yet underexplored factor shaping market behaviors and driving user preferences between centralized exchanges (CEXs) and decentralized exchanges (DEXs)…
Causal InferenceDistributed ComputingSentiment AnalysisSecuring O-RAN Open Interfaces
The next generation of cellular networks will be characterized by openness, intelligence, virtualization, and distributed computing. The Open Radio Access Network (Open RAN) framework represents a significant leap toward…
Distributed ComputingThe intelligent prediction and assessment of financial information risk in the cloud computing model
Cloud computing (cloud computing) is a kind of distributed computing, referring to the network "cloud" will be a huge data calculation and processing program into countless small programs, and then, through the system co…
Cloud ComputingDistributed ComputingManagementGoing Forward-Forward in Distributed Deep Learning
We introduce a new approach in distributed deep learning, utilizing Geoffrey Hinton's Forward-Forward (FF) algorithm to speed up the training of neural networks in distributed computing environments. Unlike traditional m…
Deep LearningDistributed ComputingEnhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning
While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distri…
Distributed ComputingFederated LearningPrivacy PreservingPeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning
Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict …
Deep Reinforcement LearningDistributed Computingreinforcement-learningReinforcement Learning+1Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning
This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the…
Cloud ComputingDistributed ComputingEdge-computingFault DetectionA Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades. In t…
Distributed ComputingMGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning
As a new distributed computing framework that can protect data privacy, federated learning (FL) has attracted more and more attention in recent years. It receives gradients from users to train the global model and releas…
Distributed ComputingEdge DetectionFederated LearningImage ReconstructionComputational Offloading in Semantic-Aware Cloud-Edge-End Collaborative Networks
The trend of massive connectivity pushes forward the explosive growth of end devices. The emergence of various applications has prompted a demand for pervasive connectivity and more efficient computing paradigms. On the …
Cloud ComputingDeep Reinforcement LearningDistributed ComputingEdge-computing+1Multiple Access in the Era of Distributed Computing and Edge Intelligence
This paper focuses on the latest research and innovations in fundamental next-generation multiple access (NGMA) techniques and the coexistence with other key technologies for the sixth generation (6G) of wireless network…
Distributed ComputingEdge-computingFederated LearningConsensus learning: A novel decentralised ensemble learning paradigm
The widespread adoption of large-scale machine learning models in recent years highlights the need for distributed computing for efficiency and scalability. This work introduces a novel distributed machine learning parad…
Distributed ComputingEnsemble LearningScalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach
In the rapidly evolving domain of electrical power systems, the Volt-VAR optimization (VVO) is increasingly critical, especially with the burgeoning integration of renewable energy sources. Traditional approaches to lear…
Deep Reinforcement LearningDistributed Computingreinforcement-learningReinforcement Learning+1