Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Convergence of Byzantine-Resilient Gradient Tracking via Probabilistic Edge Dropout
We study distributed optimization over networks with Byzantine agents that may send arbitrary adversarial messages. We propose \emph{Gradient Tracking with Probabilistic Edge Dropout} (GT-PD), a stochastic gradient track…
Distributed OptimizationADMM-Based Distributed MPC with Control Barrier Functions for Safe Multi-Robot Quadrupedal Locomotion
This paper proposes a fully decentralized model predictive control (MPC) framework with control barrier function (CBF) constraints for safety-critical trajectory planning in multi-robot legged systems. The incorporation …
Distributed OptimizationTrajectory PlanningFederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios
Federated Learning (FL) enables distributed optimization without compromising data sovereignty. Yet, where local label distributions are mutually exclusive, standard weight aggregation fails due to conflicting optimizati…
Distributed OptimizationFederated LearningOne-Shot LearningA New Kernel Regularity Condition for Distributed Mirror Descent: Broader Coverage and Simpler Analysis
Existing convergence of distributed optimization methods in non-Euclidean geometries typically rely on kernel assumptions: (i) global Lipschitz smoothness and (ii) bi-convexity of the associated Bregman divergence functi…
Distributed OptimizationByzantine-Robust Optimization under $(L_0, L_1)$-Smoothness
We consider distributed optimization under Byzantine attacks in the presence of $(L_0,L_1)$-smoothness, a generalization of standard $L$-smoothness that captures functions with state-dependent gradient Lipschitz constant…
Distributed OptimizationReasoning as Gradient: Scaling MLE Agents Beyond Tree Search
LLM-based agents for machine learning engineering (MLE) predominantly rely on tree search, a form of gradient-free optimization that uses scalar validation scores to rank candidates. As LLM reasoning capabilities improve…
Distributed OptimizationriMESA: Consensus ADMM for Real-World Collaborative SLAM
Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for multi-robot teams as it enables downstream tasks like planning and navigation. However, existing C-SLAM back-end algorithms tha…
Distributed OptimizationA distributed semismooth Newton based augmented Lagrangian method for distributed optimization
This paper proposes a novel distributed semismooth Newton based augmented Lagrangian method for solving a class of optimization problems over networks, where the global objective is defined as the sum of locally held cos…
Distributed OptimizationDistributed Quantum Gaussian Processes for Multi-Agent Systems
Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale real-world domains due to the limited expressivity of classical kernels. Quantum…
Distributed OptimizationGaussian ProcessesFrom $O(mn)$ to $O(r^2)$: Two-Sided Low-Rank Communication for Adam in Distributed Training with Memory Efficiency
As foundation models continue to scale, pretraining increasingly relies on data-parallel distributed optimization, making bandwidth-limited gradient synchronization a key bottleneck. Orthogonally, projection-based low-ra…
Distributed OptimizationFrom Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity
Standard federated learning algorithms are vulnerable to adversarial nodes, a.k.a. Byzantine failures. To solve this issue, robust distributed learning algorithms have been developed, which typically replace parameter av…
Distributed OptimizationFederated LearningDo We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods
Modern distributed optimization methods mostly rely on traditional synchronous approaches, despite substantial recent progress in asynchronous optimization. We revisit Synchronous SGD and its robust variant, called $m$-S…
Distributed OptimizationSimulation-based Bayesian inference with ameliorative learned summary statistics -- Part I
This paper, which is Part 1 of a two-part paper series, considers a simulation-based inference with learned summary statistics, in which such a learned summary statistic serves as an empirical-likelihood with ameliorativ…
Distributed OptimizationBayesian InferenceCollaborative Compressors in Distributed Mean Estimation with Limited Communication Budget
Distributed high dimensional mean estimation is a common aggregation routine used often in distributed optimization methods. Most of these applications call for a communication-constrained setting where vectors, whose me…
Distributed OptimizationBiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training
Slow and costly communication is often the main bottleneck in distributed optimization, especially in federated learning where it occurs over wireless networks. We introduce BiCoLoR, a communication-efficient optimizatio…
Distributed OptimizationFederated LearningProvably Convergent Decentralized Optimization over Directed Graphs under Generalized Smoothness
Decentralized optimization has become a fundamental tool for large-scale learning systems; however, most existing methods rely on the classical Lipschitz smoothness assumption, which is often violated in problems with ra…
Distributed OptimizationLocal Updates in Distributed Optimization: Provable Acceleration and Topology Effects
Inspired by the success of performing multiple local optimization steps between communication rounds in federated learning, incorporating such local updates into distributed optimization has recently attracted growing in…
Distributed OptimizationFederated LearningRIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization
This chapter overviews the concept of Smart Wireless Environments (SWEs) motivated by the emerging technology of Reconfigurable Intelligent Surfaces (RISs). The operating principles and state-of-the-art hardware architec…
Distributed OptimizationSemantic-Constrained Federated Aggregation: Convergence Theory and Privacy-Utility Bounds for Knowledge-Enhanced Distributed Learning
Federated learning enables collaborative model training across distributed data sources but suffers from slow convergence under non-IID data conditions. Existing solutions employ algorithmic modifications treating all cl…
Distributed OptimizationFederated LearningKnowledge GraphsDistributed Dynamic Associative Memory via Online Convex Optimization
An associative memory (AM) enables cue-response recall, and it has recently been recognized as a key mechanism underlying modern neural architectures such as Transformers. In this work, we introduce the concept of distri…
Distributed Optimization