Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Towards safe control parameter tuning in distributed multi-agent systems
Many safety-critical real-world problems, such as autonomous driving and collaborative robots, are of a distributed multi-agent nature. To optimize the performance of these systems while ensuring safety, we can cast them…
Distributed OptimizationAutonomous DrivingCommunication-Efficient Distributed Asynchronous ADMM
In distributed optimization and federated learning, asynchronous alternating direction method of multipliers (ADMM) serves as an attractive option for large-scale optimization, data privacy, straggler nodes and variety o…
Distributed OptimizationFederated LearningIntegrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment
This paper addresses the challenges of data privacy and collaborative modeling in cross-institution financial risk analysis. It proposes a risk assessment framework based on federated learning. Without sharing raw data, …
Distributed OptimizationFederated LearningDistributed optimization: designed for federated learning
Federated learning (FL), as a distributed collaborative machine learning (ML) framework under privacy-preserving constraints, has garnered increasing research attention in cross-organizational data collaboration scenario…
Distributed OptimizationComputational EfficiencyFederated LearningGreedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
Distributed optimization is pivotal for large-scale signal processing and machine learning, yet communication overhead remains a major bottleneck. Low-rank gradient compression, in which the transmitted gradients are app…
Distributed OptimizationCommunication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching
Federated learning with differential privacy suffers from two major costs: each client must transmit $d$-dimensional gradients every round, and the magnitude of DP noise grows with $d$. Yet empirical studies show that gr…
Distributed OptimizationFederated LearningDPLib: A Standard Benchmark Library for Distributed Power System Analysis and Optimization
\textit{DPLib} is an open-source MATLAB-based benchmark library created to support research and development in distributed and decentralized power system analysis and optimization. Distributed and decentralized methods o…
Distributed OptimizationMulti-Timescale Gradient Sliding for Distributed Optimization
We propose two first-order methods for convex, non-smooth, distributed optimization problems, hereafter called Multi-Timescale Gradient Sliding (MT-GS) and its accelerated variant (AMT-GS). Our MT-GS and AMT-GS can take …
Distributed OptimizationDecentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking
This paper considers the problem of decentralized optimization on compact submanifolds, where a finite sum of smooth (possibly non-convex) local functions is minimized by $n$ agents forming an undirected and connected gr…
Distributed OptimizationQuantizationDistributed gradient methods under heavy-tailed communication noise
We consider a standard distributed optimization problem in which networked nodes collaboratively minimize the sum of their locally known convex costs. For this setting, we address for the first time the fundamental probl…
Distributed OptimizationOnline distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading
The proliferation of distributed renewable energy triggers the peer-to-peer (P2P) energy market formations. To make profits, prosumers equipped with photovoltaic (PV) panels and even the energy storage system (ESS) can a…
Distributed Optimizationenergy tradingStochastic OptimizationPrivacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations
The massive integration of uncertain distributed renewable energy resources into power systems raises power imbalance concerns. Peer-to-peer (P2P) energy trading provides a promising way to balance the prosumers' volatil…
Distributed Optimizationenergy tradingPrivacy PreservingFederated Learning: From Theory to Practice
This book offers a hands-on introduction to building and understanding federated learning (FL) systems. FL enables multiple devices -- such as smartphones, sensors, or local computers -- to collaboratively train machine …
Distributed OptimizationFederated LearningPrivacy PreservingBeyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target d…
Distributed OptimizationGraph SamplingLAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimization
We propose LAGO - Language Similarity-Aware Graph Optimization - a novel approach for few-shot cross-lingual embedding inversion attacks, addressing critical privacy vulnerabilities in multilingual NLP systems. Unlike pr…
Distributed OptimizationMultilingual NLPPrivacy PreservingDistributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping
In modern large-scale systems with sensor networks and IoT devices it is essential to collaboratively solve complex problems while utilizing network resources efficiently. In our paper we present three distributed optimi…
Distributed OptimizationQuantizationSensor FusionResidual-Evasive Attacks on ADMM in Distributed Optimization
This paper presents two attack strategies designed to evade detection in ADMM-based systems by preventing significant changes to the residual during the attacked iteration. While many detection algorithms focus on identi…
Distributed OptimizationHessian Riemannian Flow For Multi-Population Wardrop Equilibrium
In this paper, we address the problem of optimizing flows on generalized graphs that feature multiple entry points and multiple populations, each with varying cost structures. We tackle this problem by considering the mu…
Distributed OptimizationManagementDistributed model predictive control without terminal cost under inexact distributed optimization
This paper presents a novel distributed model predictive control (MPC) formulation without terminal cost and a corresponding distributed synthesis approach for distributed linear discrete-time systems with coupled constr…
Distributed OptimizationModel Predictive ControlDistributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks
In this paper, we address the distributed optimization problem over unidirectional networks with possibly time-invariant heterogeneous bounded transmission delays. In particular, we propose a modified version of the Acce…
Distributed Optimization