Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Communication Reduction via Semantic-Based Encoding in DMPC Using LSTMs
The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time …
Distributed OptimizationFirst-order Constrained Trilevel Optimization Over Distributed Networks for Robust Coreset Selection
With the rapid advancement of the Internet of Things (IoT), massive amounts of data are generated across distributed edge networks. Training models on full data incurs significant computational overhead and storage bottl…
Distributed OptimizationContinual LearningWhat's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity
Local SGD, also known as Federated Averaging, is a widely used distributed optimization algorithm. Although Local SGD often outperforms alternatives such as Mini-batch SGD in practice, theory still only partially explain…
Distributed OptimizationDecentralized Gradient Descent: Bottleneck Regimes and Budget Complexity
Decentralized gradient descent (DGD) is widely used for solving distributed optimization problems over networks of agents. While its convergence properties are well understood, less is known about the communication and c…
Distributed OptimizationLearning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups
Modern robotic perception increasingly involves large-scale geometric optimization problems distributed across multiple robots or sessions. However, existing distributed solvers often depend on brittle hand tuning and pr…
Distributed OptimizationCan Model Merging Improve Aggregation in DiLoCo?
Model merging techniques, which aggregate independently finetuned models into one to combine their capabilities, have become a topic of significant interest in recent years, with a broad array of methods having been prop…
Distributed OptimizationSecond-Order KKT Guarantees for Bregman ADMM in Nonconvex and Non-Lipschitz Optimization
We analyze Bregman ADMM for nonconvex linearly constrained problems under two-sided relative smoothness, a condition that replaces the standard Lipschitz gradient assumption with a Hessian comparison relative to a Bregma…
Distributed OptimizationDeep-Unfolded Coordination
Distributed optimization is a highly scalable and structurally transparent technique to solve multi-agent robotics problems; however, such methods often suffer from the need for highly-specialized, problem-specific hyper…
Distributed OptimizationA Distributed Multi-UGV Exploration Framework With Loop-Aware Planning and Descriptor-Aided Localization in Resource-Limited Environments
Robust and efficient cooperative exploration with multiple unmanned ground vehicles (UGVs) in unknown, GPSdenied, and bandwidth-limited environments without prior maps remains challenging, as localization drift degrades …
Distributed OptimizationQuantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry
We study distributed optimization with stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD, a quantized stochastic primal-dual method, and analyze it under relax…
Distributed OptimizationA Note on Stability for Orthogonalized Matrix Momentum with Client Sampling
We study finite-sample generalization for a client-sampled distributed optimization scheme with matrix-valued parameters and orthogonalized momentum updates. The central quantity is the gap between the population and emp…
Distributed OptimizationLocal MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
Communication overhead is a crucial bottleneck in scalable distributed learning. While existing methods aim to efficiently utilize data points, such as Local SGD, Minibatch SGD, and their accelerated variants, they still…
Distributed OptimizationA Tight Theory of Error Feedback Algorithms in Distributed Optimization
Communication costs are a major bottleneck in distributed learning and first-order optimization. A common approach to alleviate this issue is to compress the gradient information exchanged between agents. However, such c…
Distributed OptimizationPrivacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms
In distributed optimization, multiple parties collaborate to find an optimal solution to a problem. Privacy-preserving distributed optimization uses techniques, such as secure multi-party computation (MPC), to protect th…
Distributed OptimizationA Prototyping Framework for Distributed Control of Multi-Robot Systems
This paper presents a prototyping framework for distributed control of multi-robot systems, aimed at bridging theory and practical testing of distributed optimization algorithms. Using the Single Program, Multiple Data (…
Distributed OptimizationRescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
Asynchronous stochastic gradient descent (ASGD) is a standard way to exploit heterogeneous compute resources in distributed learning: instead of forcing fast workers to wait for slow ones, the server updates the model wh…
Distributed OptimizationJoint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoV
This paper investigates a multi-Unmanned Aerial Vehicle (UAV) joint base station-assisted Internet of Vehicles (IoV) task offloading system in dense urban environments. To minimize system delay and energy consumption und…
Multi-agent Reinforcement LearningDistributed OptimizationHierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design
Federated learning (FL) is fundamentally a distributed optimization problem executed by communicating agents with local data, local computation, and partial system visibility. Once FL is viewed through that lens, hierarc…
Distributed OptimizationFederated LearningByzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
Byzantine-robust distributed optimization relies on robust aggregation rules to mitigate the influence of malicious Byzantine workers. Despite the proliferation of such rules, a unified convergence analysis framework tha…
Distributed OptimizationF3DGS: Federated 3D Gaussian Splatting for Decentralized Multi-Agent World Modeling
We present F3DGS, a federated 3D Gaussian Splatting framework for decentralized multi-agent 3D reconstruction. Existing 3DGS pipelines assume centralized access to all observations, which limits their applicability in di…
Distributed Optimization3D ReconstructionPoint Clouds