Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game
The Industrial Internet of Things (IIoT) leverages Federated Learning (FL) for distributed model training while preserving data privacy, and meta-computing enhances FL by optimizing and integrating distributed computing …
Deep Reinforcement LearningDistributed ComputingFederated LearningDistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
Distributed DNN inference is becoming increasingly important as the demand for intelligent services at the network edge grows. By leveraging the power of distributed computing, edge devices can perform complicated and re…
Autonomous VehiclesCollaborative InferenceDistributed ComputingGeneral Coded Computing in a Probabilistic Straggler Regime
Coded computing has demonstrated promising results in addressing straggler resiliency in distributed computing systems. However, most coded computing schemes are designed for exact computation, requiring the number of re…
Distributed ComputingByzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing
Federated learning (FL) has gained attention as a distributed learning paradigm for its data privacy benefits and accelerated convergence through parallel computation. Traditional FL relies on a server-client (SC) archit…
AllDistributed ComputingFederated LearningVehicular Multi-Tier Distributed Computing with Hybrid THz-RF Transmission in Satellite-Terrestrial Integrated Networks
In this paper, we propose a Satellite-Terrestrial Integrated Network (STIN) assisted vehicular multi-tier distributed computing (VMDC) system leveraging hybrid terahertz (THz) and radio frequency (RF) communication techn…
Distributed ComputingEdge-computingSecure Resource Allocation via Constrained Deep Reinforcement Learning
The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure…
Deep Reinforcement LearningDistributed ComputingEdge-computingreinforcement-learning+1Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
The rise of End-Edge-Cloud Collaboration (EECC) offers a promising paradigm for Artificial Intelligence (AI) model training across end devices, edge servers, and cloud data centers, providing enhanced reliability and red…
Distributed ComputingFederated LearningModel OptimizationTransfer LearningDistributed Mixture-of-Agents for Edge Inference with Large Language Models
Mixture-of-Agents (MoA) has recently been proposed as a method to enhance performance of large language models (LLMs), enabling multiple individual LLMs to work together for collaborative inference. This collaborative ap…
Collaborative InferenceDistributed ComputingComputation-and-Communication Efficient Coordinated Multicast Beamforming in Massive MIMO Networks
The main challenges in designing downlink coordinated multicast beamforming in massive multiple-input multiple output (MIMO) cellular networks are the complex computational solutions and significant fronthaul overhead fo…
Distributed ComputingA Survey on Inference Optimization Techniques for Mixture of Experts Models
The emergence of large-scale Mixture of Experts (MoE) models represents a significant advancement in artificial intelligence, offering enhanced model capacity and computational efficiency through conditional computation.…
Computational EfficiencyDistributed ComputingInference OptimizationKnowledge Distillation+4AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP
With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing …
Anomaly DetectionDistributed ComputingEdge-computingSchedulingCrossVIT-augmented Geospatial-Intelligence Visualization System for Tracking Economic Development Dynamics
Timely and accurate economic data is crucial for effective policymaking. Current challenges in data timeliness and spatial resolution can be addressed with advancements in multimodal sensing and distributed computing. We…
Distributed ComputingQuantum-Train-Based Distributed Multi-Agent Reinforcement Learning
In this paper, we introduce Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning (Dist-QTRL), a novel approach to addressing the scalability challenges of traditional Reinforcement Learning (RL) by integrat…
Distributed ComputingMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1Deep Learning, Machine Learning, Advancing Big Data Analytics and Management
Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for research and application. This work explores th…
Anomaly DetectionDeep LearningDimensionality ReductionDistributed Computing+4Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows
This paper presents a Spark-based modular LangGraph framework, designed to enhance machine learning workflows through scalability, visualization, and intelligent process optimization. At its core, the framework introduce…
Decision MakingDistributed ComputingFeature Engineeringgraph construction+1AI Benchmarks and Datasets for LLM Evaluation
LLMs demand significant computational resources for both pre-training and fine-tuning, requiring distributed computing capabilities due to their large model sizes \cite{sastry2024computing}. Their complex architecture po…
BenchmarkingDistributed ComputingHallucinationIterative Distributed Multinomial Regression
This article introduces an iterative distributed computing estimator for the multinomial logistic regression model with large choice sets. Compared to the maximum likelihood estimator, the proposed iterative distributed …
Computational EfficiencyDistributed ComputingregressionOptimizing Airline Reservation Systems with Edge-Enabled Microservices: A Framework for Real-Time Data Processing and Enhanced User Responsiveness
The growing complexity of the operations of airline reservations requires a smart solution for the adoption of novel approaches to the development of quick, efficient, and adaptive reservation systems. This paper outline…
Distributed ComputingEdge-computingBody-Resonance Human Body Communication
Seamless interaction between Humans and AI-empowered battery-operated miniaturized electronic devices, exponentially transforming the wearable technology industry while forming an anthropomorphic artificial nervous syste…
Distributed ComputingOnline Parallel Multi-Task Relationship Learning via Alternating Direction Method of Multipliers
Online multi-task learning (OMTL) enhances streaming data processing by leveraging the inherent relations among multiple tasks. It can be described as an optimization problem in which a single loss function is defined fo…
Distributed ComputingMulti-Task Learning