Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs. Large-scale edge-based collection of training data in these applications c…
Distributed OptimizationFederated LearningAcceleration of Federated Learning with Alleviated Forgetting in Local Training
Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, ther…
Distributed OptimizationFederated LearningDistributed Methods with Absolute Compression and Error Compensation
Distributed optimization methods are often applied to solving huge-scale problems like training neural networks with millions and even billions of parameters. In such applications, communicating full vectors, e.g., (stoc…
Distributed OptimizationDistributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control
This article presents a distributed model-predictive control (MPC) design for real-time voltage control in power systems, including an online method to estimate the bus admittance matrix $\mathbf{Y}$ to let it be time-va…
Anomaly DetectionDistributed OptimizationModel Predictive ControlMulti-objective Distributed Optimization for Zonal Distribution System with Multi-Microgrids
The issue of voltage variations caused by integration of renewables has been addressed in this paper through distributed management of Microgrids (MGs). The distribution network (DN) takes the network losses and voltage …
Distributed OptimizationManagementSignal Decomposition Using Masked Proximal Operators
We consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We describe a simple and general framework…
Distributed OptimizationTime SeriesTime Series AnalysisEscaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning
In recent centralized nonconvex distributed learning and federated learning, local methods are one of the promising approaches to reduce communication time. However, existing work has mainly focused on studying first-ord…
Distributed OptimizationFederated LearningSHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scenarios where the network is strongly heter…
Distributed OptimizationFederated LearningDistributed saddle point problems for strongly concave-convex functions
In this paper, we propose GT-GDA, a distributed optimization method to solve saddle point problems of the form: $\min_{\mathbf{x}} \max_{\mathbf{y}} \{F(\mathbf{x},\mathbf{y}) :=G(\mathbf{x}) + \langle \mathbf{y}, \overl…
Distributed OptimizationSpatial Reuse in Dense Wireless Areas: A Cross-layer Optimization Approach via ADMM
This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their \newt{dist…
Distributed OptimizationCommunication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting
The challenge of communication-efficient distributed optimization has attracted attention in recent years. In this paper, a communication efficient algorithm, called ordering-based alternating direction method of multipl…
Distributed OptimizationFederated LearningDASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization
We develop and analyze DASHA: a new family of methods for nonconvex distributed optimization problems. When the local functions at the nodes have a finite-sum or an expectation form, our new methods, DASHA-PAGE and DASHA…
Distributed OptimizationFederated LearningRecycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?
In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank characteristics of the subspace spanned by gr…
Distributed OptimizationFederated LearningFederated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning
Federated learning (FL) has been intensively investigated in terms of communication efficiency, privacy, and fairness. However, efficient annotation, which is a pain point in real-world FL applications, is less studied. …
Active LearningDistributed OptimizationFairnessFederated Learning+2End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study
Providing differentiated services to meet the unique requirements of different use cases is a major goal of the fifth generation (5G) telecommunication networks and will be even more critical for future 6G systems. Fulfi…
Distributed OptimizationDistributed gradient-based optimization in the presence of dependent aperiodic communication
Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence of unreliable communication networks, t…
Distributed OptimizationDistributed Learning of Generalized Linear Causal Networks
We consider the task of learning causal structures from data stored on multiple machines, and propose a novel structure learning method called distributed annealing on regularized likelihood score (DARLS) to solve this p…
Distributed OptimizationCoordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework
This work presents an optimization framework to aggregate the power and energy flexibilities in an interconnected power distribution systems. The aggregation framework is used to compute the day-ahead dispatch plans of m…
Distributed OptimizationStochastic OptimizationDistributed Random Reshuffling over Networks
In this paper, we consider distributed optimization problems where $n$ agents, each possessing a local cost function, collaboratively minimize the average of the local cost functions over a connected network. To solve th…
Distributed OptimizationConvergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems
There are much recent interests in solving noncovnex min-max optimization problems due to its broad applications in many areas including machine learning, networked resource allocations, and distributed optimization. Per…
Distributed Optimization