Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates
Optimizing problems in a distributed manner is critical for systems involving multiple agents with private data. Despite substantial interest, a unified method for analyzing the convergence rates of distributed optimizat…
Distributed OptimizationPeer-to-Peer Learning Dynamics of Wide Neural Networks
Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving manner without the aid of a central server.…
Distributed OptimizationPrivacy PreservingExploring Scaling Laws for Local SGD in Large Language Model Training
This paper investigates scaling laws for local SGD in LLM training, a distributed optimization algorithm that facilitates training on loosely connected devices. Through extensive experiments, we show that local SGD achie…
Distributed OptimizationEdge-computingLanguage ModelingLanguage Modelling+1Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections
In this paper, we consider the problem of coordinating traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We aim to develop an optimization-based control framework that leverage…
Distributed OptimizationManagementADMM for Downlink Beamforming in Cell-Free Massive MIMO Systems
In cell-free massive MIMO systems with multiple distributed access points (APs) serving multiple users over the same time-frequency resources, downlink beamforming is done through spatial precoding. Precoding vectors can…
Distributed OptimizationDistributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement
In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms…
Distributed OptimizationQuantizationAttentionX: Exploiting Consensus Discrepancy In Attention from A Distributed Optimization Perspective
In this paper, we extend the standard Attention in transformer by exploiting the consensus discrepancy from a distributed optimization perspective, referred to as AttentionX. It is noted that the primal-dual method of mu…
Distributed OptimizationSeamless Integration: Sampling Strategies in Federated Learning Systems
Federated Learning (FL) represents a paradigm shift in the field of machine learning, offering an approach for a decentralized training of models across a multitude of devices while maintaining the privacy of local data.…
Distributed OptimizationFederated LearningPrescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication
This paper addresses distributed constrained multiobjective resource allocation problems (DCMRAPs) within multi-agent networks, where each agent has multiple, potentially conflicting local objectives, constrained by both…
Decision MakingDistributed OptimizationMultiobjective OptimizationGNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network
Unmanned Aerial Vehicles (UAVs), due to their low cost and high flexibility, have been widely used in various scenarios to enhance network performance. However, the optimization of UAV trajectories in unknown areas or ar…
Distributed OptimizationEfficient Neural NetworkManagementTrajectory PlanningA survey on secure decentralized optimization and learning
Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new priva…
Decision MakingDistributed OptimizationPrivacy PreservingSurveyDistributed Optimization by Network Flows with Spatio-Temporal Compression
Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information…
Distributed OptimizationSpatio-Temporal Communication Compression in Distributed Prime-Dual Flows
In this paper, we study distributed prime-dual flows for multi-agent optimization with spatio-temporal compressions. The central aim of multi-agent optimization is for a network of agents to collaboratively solve a syste…
Distributed OptimizationDistributed Difference of Convex Optimization
In this article, we focus on solving a class of distributed optimization problems involving $n$ agents with the local objective function at every agent $i$ given by the difference of two convex functions $f_i$ and $g_i$ …
Distributed OptimizationA Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent
Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several workers. However, scant attention h…
Distributed OptimizationCooperative Integrated Sensing and Communication Networks: Analysis and Distributed Design
This paper proposes a cooperative integrated sensing and communication network (Co-ISACNet) adopting hybrid beamforming (HBF) architecture, which improves both radar sensing and communication performance. The main contri…
Distributed OptimizationIntegrated sensing and communicationCommunication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks
We provide a communication- and computation-efficient method for distributed submodular optimization in robot mesh networks. Submodularity is a property of diminishing returns that arises in active information gathering …
Decision MakingDistributed OptimizationMotion PlanningRAGProvable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization
Federated learning (FL) emerged as a paradigm designed to improve data privacy by enabling data to reside at its source, thus embedding privacy as a core consideration in FL architectures, whether centralized or decentra…
Distributed OptimizationFederated LearningFast Distributed Optimization over Directed Graphs under Malicious Attacks using Trust
In this work, we introduce the Resilient Projected Push-Pull (RP3) algorithm designed for distributed optimization in multi-agent cyber-physical systems with directed communication graphs and the presence of malicious ag…
Distributed OptimizationGraphon Particle Systems, Part II: Dynamics of Distributed Stochastic Continuum Optimization
We study the distributed optimization problem over a graphon with a continuum of nodes, which is regarded as the limit of the distributed networked optimization as the number of nodes goes to infinity. Each node has a pr…
Distributed OptimizationLEMMA