Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Distributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization
This paper introduces a new approach that leverages Multi-agent Bayesian Optimization (MABO) to design Distributed Model Predictive Control (DMPC) schemes for multi-agent systems. The primary objective is to learn optima…
Bayesian OptimizationDistributed OptimizationModel Predictive ControlDistributed Convex Optimization with State-Dependent (Social) Interactions over Random Networks
This paper aims at distributed multi-agent convex optimization where the communications network among the agents are presented by a random sequence of possibly state-dependent weighted graphs. This is the first work to c…
Distributed OptimizationFedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning
Federated learning (FL) is a promising technology for data privacy and distributed optimization, but it suffers from data imbalance and heterogeneity among clients. Existing FL methods try to solve the problems by aligni…
counterfactualDistributed OptimizationFederated LearningAccelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
In recent years, as data and problem sizes have increased, distributed learning has become an essential tool for training high-performance models. However, the communication bottleneck, especially for high-dimensional da…
Distributed OptimizationTowards privacy-preserving cooperative control via encrypted distributed optimization
Cooperative control is crucial for the effective operation of dynamical multi-agent systems. Especially for distributed control schemes, it is essential to exchange data between the agents. This becomes a privacy threat …
Distributed OptimizationPrivacy PreservingInformation-Geometric Barycenters for Bayesian Federated Learning
Federated learning (FL) is a widely used and impactful distributed optimization framework that achieves consensus through averaging locally trained models. While effective, this approach may not align well with Bayesian …
Bayesian InferenceDistributed OptimizationFairnessFederated Learning+1Deep Distributed Optimization for Large-Scale Quadratic Programming
Quadratic programming (QP) forms a crucial foundation in optimization, encompassing a broad spectrum of domains and serving as the basis for more advanced algorithms. Consequently, as the scale and complexity of modern a…
Distributed OptimizationGeneralization BoundsFractional Order Distributed Optimization
Distributed optimization is fundamental to modern machine learning applications like federated learning, but existing methods often struggle with ill-conditioned problems and face stability-versus-speed tradeoffs. We int…
Distributed OptimizationFederated LearningReview of Mathematical Optimization in Federated Learning
Federated Learning (FL) has been becoming a popular interdisciplinary research area in both applied mathematics and information sciences. Mathematically, FL aims to collaboratively optimize aggregate objective functions …
Distributed OptimizationFederated LearningProblem-dependent convergence bounds for randomized linear gradient compression
In distributed optimization, the communication of model updates can be a performance bottleneck. Consequently, gradient compression has been proposed as a means of increasing optimization throughput. In general, due to i…
Distributed Optimizationimage-classificationImage ClassificationStochastic OptimizationOptimization Algorithm Design via Electric Circuits
We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is to design an appropriate electric circu…
Distributed OptimizationLogarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks
Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication networks, particularly limited bandwidth, n…
Distributed OptimizationQuantizationTighter Performance Theory of FedExProx
We revisit FedExProx - a recently proposed distributed optimization method designed to enhance convergence properties of parallel proximal algorithms via extrapolation. In the process, we uncover a surprising flaw: its k…
Distributed OptimizationDiversityFederated LearningByzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach
How to achieve precise distributed optimization despite unknown attacks, especially the Byzantine attacks, is one of the critical challenges for multiagent systems. This paper addresses a distributed resilient optimizati…
Distributed OptimizationvalidFedECADO: A Dynamical System Model of Federated Learning
Federated learning harnesses the power of distributed optimization to train a unified machine learning model across separate clients. However, heterogeneous data distributions and computational workloads can lead to inco…
Distributed OptimizationFederated LearningmodelSensitivitySingle Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function
Zero-order (ZO) optimization is a powerful tool for dealing with realistic constraints. On the other hand, the gradient-tracking (GT) technique proved to be an efficient method for distributed optimization aiming to achi…
Distributed OptimizationAiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information
Federated learning has emerged in the last decade as a distributed optimization paradigm due to the rapidly increasing number of portable devices able to support the heavy computational needs related to the training of m…
Distributed OptimizationFederated LearningA Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis
The training of classification models for fault diagnosis tasks using geographically dispersed data is a crucial task for original equipment manufacturers (OEMs) seeking to provide long-term service contracts (LTSCs) to …
Distributed OptimizationFault DiagnosisNewton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing
Motivated by recent advances in serverless cloud computing, in particular the "function as a service" (FaaS) model, we consider the problem of minimizing a convex function in a massively parallel fashion, where communica…
Cloud ComputingDistributed OptimizationA Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters
A distributed controller for secondary control problems in microgrids with grid-forming (GFM) inverter-based resources (IBRs) is developed. The controller is based on distributed optimization and is synthesized and imple…
Distributed Optimization