Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Communication-Efficient Distributed SGD with Compressed Sensing
We consider large scale distributed optimization over a set of edge devices connected to a central server, where the limited communication bandwidth between the server and edge devices imposes a significant bottleneck fo…
compressed sensingDistributed OptimizationFederated LearningDistributed Graph Learning with Smooth Data Priors
Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the grap…
Distributed OptimizationGraph LearningvalidCollaborative Learning over Wireless Networks: An Introductory Overview
In this chapter, we will mainly focus on collaborative training across wireless devices. Training a ML model is equivalent to solving an optimization problem, and many distributed optimization algorithms have been develo…
Distributed OptimizationVariance Reduction in Deep Learning: More Momentum is All You Need
Variance reduction (VR) techniques have contributed significantly to accelerating learning with massive datasets in the smooth and strongly convex setting (Schmidt et al., 2017; Johnson & Zhang, 2013; Roux et al., 2012).…
AllData AugmentationDeep LearningDistributed OptimizationFLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
Federated Learning (FL) is an increasingly popular machine learning paradigm in which multiple nodes try to collaboratively learn under privacy, communication and multiple heterogeneity constraints. A persistent problem …
Distributed OptimizationFederated LearningA Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections
In this paper, we consider the optimal coordination of automated vehicles at intersections under fixed crossing orders. We formulate the problem using direct optimal control and exploit the structure to construct a semi-…
Distributed OptimizationFinite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping
Distributed learning has become an integral tool for scaling up machine learning and addressing the growing need for data privacy. Although more robust to the network topology, decentralized learning schemes have not gai…
AttributeDistributed OptimizationBasis Matters: Better Communication-Efficient Second Order Methods for Federated Learning
Recent advances in distributed optimization have shown that Newton-type methods with proper communication compression mechanisms can guarantee fast local rates and low communication cost compared to first order methods. …
Distributed OptimizationFederated LearningSecond-order methodsDecentralized Feature-Distributed Optimization for Generalized Linear Models
We consider the "all-for-one" decentralized learning problem for generalized linear models. The features of each sample are partitioned among several collaborating agents in a connected network, but only one agent observ…
Distributed OptimizationCell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach
Cell zooming has been becoming an essential enabler for off-grid small cell networks. Traditional models often utilize the numbers of active users in order to determine cell zooming strategies. However, such confidential…
Distributed OptimizationParallel Feedforward Compensation for Output Synchronization: Fully Distributed Control and Indefinite Laplacian
This work is associated with the use of parallel feedforward compensators (PFCs) for the problem of output synchronization over heterogeneous agents and the benefits this approach can provide. Specifically, it addresses …
Distributed OptimizationAcceleration in Distributed Optimization under Similarity
We study distributed (strongly convex) optimization problems over a network of agents, with no centralized nodes. The loss functions of the agents are assumed to be \textit{similar}, due to statistical data similarity or…
Distributed OptimizationA Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow
With the increasing penetration of distributed energy resources, distributed optimization algorithms have attracted significant attention for power systems applications due to their potential for superior scalability, pr…
Distributed OptimizationQ-Learningreinforcement-learningReinforcement Learning (RL)Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning
This paper considers the problem of resilient distributed optimization and stochastic machine learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has a local cost …
Distributed OptimizationDistributed Optimization of Graph Convolutional Network using Subgraph Variance
In recent years, Graph Convolutional Networks (GCNs) have achieved great success in learning from graph-structured data. With the growing tendency of graph nodes and edges, GCN training by single processor cannot meet th…
Distributed OptimizationKKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey
This is a tutorial and survey paper on Karush-Kuhn-Tucker (KKT) conditions, first-order and second-order numerical optimization, and distributed optimization. After a brief review of history of optimization, we start wit…
Distributed OptimizationMultiobjective OptimizationSecond-order methodsStochastic OptimizationDistributed Privacy-Preserving Electric Vehicle Charging Control Based on Secret Sharing
Cooperative electric vehicle (EV) charging control has emerged as a key component in grid-edge resource (GER) management. However, customers' privacy remains a major barrier to large-scale implementation of EV charging c…
Distributed OptimizationManagementPrivacy PreservingDistributed Optimization using Heterogeneous Compute Systems
Hardware compute power has been growing at an unprecedented rate in recent years. The utilization of such advancements plays a key role in producing better results in less time -- both in academia and industry. However, …
Distributed OptimizationUnbiased Single-scale and Multi-scale Quantizers for Distributed Optimization
Massive amounts of data have led to the training of large-scale machine learning models on a single worker inefficient. Distributed machine learning methods such as Parallel-SGD have received significant interest as a so…
BIG-bench Machine LearningDistributed OptimizationQuantizationCommunication-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 LearningQuantization