Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Network-Aware Value Stacking of Community Battery via Asynchronous Distributed Optimization
Community battery systems have been widely deployed to provide services to the grid. Unlike a single battery storage system in the community, coordinating multiple community batteries can further unlock their value, enha…
Distributed OptimizationQuantization Avoids Saddle Points in Distributed Optimization
Distributed nonconvex optimization underpins key functionalities of numerous distributed systems, ranging from power systems, smart buildings, cooperative robots, vehicle networks to sensor networks. Recently, it has als…
Distributed OptimizationQuantizationStreamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction
In this study, we investigate stochastic optimization on Riemannian manifolds, focusing on the crucial variance reduction mechanism used in both Euclidean and Riemannian settings. Riemannian variance-reduced methods usua…
Distributed OptimizationRiemannian optimizationStochastic OptimizationLoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a communication-efficient algorithm that l…
Distributed OptimizationFederated LearningQuantizationMUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods
Gradient-type distributed optimization methods have blossomed into one of the most important tools for solving a minimization learning task over a networked agent system. However, only one gradient update per iteration i…
Distributed OptimizationPrivacy-Preserving Distributed Optimization and Learning
Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite rapid development, existing distributed op…
Distributed OptimizationPrivacy PreservingParallel Momentum Methods Under Biased Gradient Estimations
Parallel stochastic gradient methods are gaining prominence in solving large-scale machine learning problems that involve data distributed across multiple nodes. However, obtaining unbiased stochastic gradients, which ha…
Distributed OptimizationMeta-LearningTernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
Distributed training of deep neural networks faces three critical challenges: privacy preservation, communication efficiency, and robustness to fault and adversarial behaviors. Although significant research efforts have …
Distributed OptimizationFairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
In pursuit of fairness and balanced development, recommender systems (RS) often prioritize group fairness, ensuring that specific groups maintain a minimum level of exposure over a given period. For example, RS platforms…
Distributed OptimizationFairnessRecommendation SystemsRetrievalA Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks
Cyber-physical systems (CPSs) facilitate the integration of physical entities and cyber infrastructures through the utilization of pervasive computational resources and communication units, leading to improved efficiency…
Distributed OptimizationMiscellaneousSurveyImproving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity
Effective communication between the server and workers plays a key role in distributed optimization. In this paper, we focus on optimizing the server-to-worker communication, uncovering inefficiencies in prevalent downli…
Distributed OptimizationDistributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers
Many machine learning applications require operating on a spatially distributed dataset. Despite technological advances, privacy considerations and communication constraints may prevent gathering the entire dataset in a …
Bayesian InferenceDistributed OptimizationUncertainty QuantificationSurvey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems
Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing …
Distributed OptimizationSchedulingSurveyAsynchronous Local-SGD Training for Language Modeling
Local stochastic gradient descent (Local-SGD), also referred to as federated averaging, is an approach to distributed optimization where each device performs more than one SGD update per communication. This work presents…
Distributed OptimizationLanguage ModelingLanguage ModellingOptimal Data Splitting in Distributed Optimization for Machine Learning
The distributed optimization problem has become increasingly relevant recently. It has a lot of advantages such as processing a large amount of data in less time compared to non-distributed methods. However, most distrib…
Distributed OptimizationCorrelated Quantization for Faster Nonconvex Distributed Optimization
Quantization (Alistarh et al., 2017) is an important (stochastic) compression technique that reduces the volume of transmitted bits during each communication round in distributed model training. Suresh et al. (2022) intr…
Distributed OptimizationQuantizationConvergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs
Federated learning (FL) achieves collaborative learning without the need for data sharing, thus preventing privacy leakage. To extend FL into a fully decentralized algorithm, researchers have applied distributed optimiza…
Dimensionality ReductionDistributed OptimizationFederated LearningA Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization
Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform several iterations of local computations betw…
Distributed OptimizationAsynchronous Distributed Optimization with Delay-free Parameters
Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or use fixed step-sizes that depend on and decrease with an upper bound of the delays. Not…
Distributed OptimizationA Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events
The increasing electricity use and reliance on intermittent renewable energy sources challenge power grid management during peak demand, making Demand Response programs and energy conservation measures essential. This re…
Distributed OptimizationManagement