Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective and popular a…
Distributed OptimizationLower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
Recent advances in distributed optimization and learning have shown that communication compression is one of the most effective means of reducing communication. While there have been many results on convergence rates und…
Distributed OptimizationBeyond spectral gap: The role of the topology in decentralized learning
In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. We consider the …
Distributed OptimizationA Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting
We present a new method that includes three key components of distributed optimization and federated learning: variance reduction of stochastic gradients, partial participation, and compressed communication. We prove tha…
Distributed OptimizationFederated LearningOptimal Gradient Sliding and its Application to Distributed Optimization Under Similarity
We study structured convex optimization problems, with additive objective $r:=p + q$, where $r$ is ($\mu$-strongly) convex, $q$ is $L_q$-smooth and convex, and $p$ is $L_p$-smooth, possibly nonconvex. For such a class of…
Distributed OptimizationByzantine Machine Learning Made Easy by Resilient Averaging of Momentums
Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarant…
BIG-bench Machine LearningDistributed OptimizationDistributed Optimization in Distribution Systems with Grid-Forming and Grid-Supporting Inverters
With massive penetrations of active grid-edge technologies, distributed computing and optimization paradigm has gained significant attention to solve distribution-level optimal power flow (OPF) problems. However, the app…
Decision MakingDistributed ComputingDistributed OptimizationProblem DecompositionOn Distributed Adaptive Optimization with Gradient Compression
We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient tra…
Distributed OptimizationEF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization
In distributed or federated optimization and learning, communication between the different computing units is often the bottleneck and gradient compression is widely used to reduce the number of bits sent within each com…
Distributed OptimizationPower Bundle Adjustment for Large-Scale 3D Reconstruction
We introduce Power Bundle Adjustment as an expansion type algorithm for solving large-scale bundle adjustment problems. It is based on the power series expansion of the inverse Schur complement and constitutes a new fami…
3D ReconstructionDistributed OptimizationUnderstanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective
Distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control. Significant research efforts have been devoted to developing and anal…
Distributed OptimizationOptimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement
This paper presents a novel scheme termed Optimization-based Ramping Reserve Allocation (ORRA) for addressing an ongoing challenge in Automatic Generation Control (AGC) enhancement, i.e., the optimal coordination of mult…
Distributed OptimizationLoad ForecastingDistributed Dynamic Safe Screening Algorithms for Sparse Regularization
Distributed optimization has been widely used as one of the most efficient approaches for model training with massive samples. However, large-scale learning problems with both massive samples and high-dimensional feature…
Distributed OptimizationFedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation
Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is particularly appealing because of its abil…
Distributed OptimizationFederated LearningMulti-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point
We propose cooperative edge-assisted dynamic federated learning (CE-FL). CE-FL introduces a distributed machine learning (ML) architecture, where data collection is carried out at the end devices, while the model trainin…
Distributed OptimizationFederated LearningCompetition-Based Resilience in Distributed Quadratic Optimization
This paper proposes a novel approach to resilient distributed optimization with quadratic costs in a networked control system (e.g., wireless sensor network, power grid, robotic team) prone to external attacks (e.g., hac…
Distributed OptimizationDistributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds
We consider distributed optimization methods for problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We leverage randomized sketches for reducing the problem d…
Cloud ComputingDistributed OptimizationDistributed Dual Quaternion Based Localization of Visual Sensor Networks
In this paper we consider the localization problem for a visual sensor network. Inspired by the alternate attitude and position distributed optimization framework discussed in [1], we propose an estimation scheme that ex…
Distributed OptimizationPositionOptimal Methods for Convex Risk Averse Distributed Optimization
This paper studies the communication complexity of convex risk-averse optimization over a network. The problem generalizes the well-studied risk-neutral finite-sum distributed optimization problem and its importance stem…
Distributed OptimizationCorrelated quantization for distributed mean estimation and optimization
We study the problem of distributed mean estimation and optimization under communication constraints. We propose a correlated quantization protocol whose leading term in the error guarantee depends on the mean deviation …
Distributed OptimizationQuantization