Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
When Evolutionary Computation Meets Privacy
Recently, evolutionary computation (EC) has been promoted by machine learning, distributed computing, and big data technologies, resulting in new research directions of EC like distributed EC and surrogate-assisted EC. T…
Distributed ComputingDistributed OptimizationPrivacy PreservingOn the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing
Decentralized learning and optimization is a central problem in control that encompasses several existing and emerging applications, such as federated learning. While there exists a vast literature on this topic and most…
Distributed OptimizationFederated LearningByzantine-Robust Loopless Stochastic Variance-Reduced Gradient
Distributed optimization with open collaboration is a popular field since it provides an opportunity for small groups/companies/universities, and individuals to jointly solve huge-scale problems. However, standard optimi…
Distributed OptimizationDifferentially Private Distributed Convex Optimization
This paper considers distributed optimization (DO) where multiple agents cooperate to minimize a global objective function, expressed as a sum of local objectives, subject to some constraints. In DO, each agent iterative…
Distributed OptimizationFederated LearningPrivacy PreservingMulti-Message Shuffled Privacy in Federated Learning
We study differentially private distributed optimization under communication constraints. A server using SGD for optimization aggregates the client-side local gradients for model updates using distributed mean estimation…
Distributed OptimizationFederated LearningOpen-Ended Question AnsweringTAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation
In distributed optimization and learning, several machines alternate between local computations in parallel and communication with a distant server. Communication is usually slow and costly and forms the main bottleneck.…
Distributed OptimizationFederated LearningDistributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits
Reactive power sharing and containment of voltages within limits for inverter-based resources (IBRs) are two important, yet coupled objectives in ac networks. In this article, we propose a distributed control technique t…
Distributed OptimizationSparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning
The training efficiency of complex deep learning models can be significantly improved through the use of distributed optimization. However, this process is often hindered by a large amount of communication cost between w…
Deep LearningDistributed OptimizationQuantizationFederated Learning as Variational Inference: A Scalable Expectation Propagation Approach
The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A rece…
Distributed OptimizationFederated LearningVariational InferenceCEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence
In this paper, we consider solving the distributed optimization problem over a multi-agent network under the communication restricted setting. We study a compressed decentralized stochastic gradient method, termed ``comp…
Distributed OptimizationAlgorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks
The user-centric cell-free network has emerged as an appealing technology to improve the next-generation wireless network's capacity thanks to its ability to eliminate inter-cell interference effectively. However, the ce…
Distributed OptimizationRolling Shutter CorrectionBeyond spectral gap (extended): 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. In the decentral…
Distributed OptimizationMachine Learning for Large-Scale Optimization in 6G Wireless Networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified …
Computational EfficiencyDeep Reinforcement LearningDistributed OptimizationFederated Learning+3Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits
Multi-player multi-armed bandit is an increasingly relevant decision-making problem, motivated by applications to cognitive radio systems. Most research for this problem focuses exclusively on the settings that players h…
Decision MakingDistributed OptimizationSimulation-Integrated Distributed Optimal Power Flow for Unbalanced Power Distribution Systems
Distributed optimization methods have been extensively applied for the optimization of electric power distribution systems, especially for grid-edge coordination. Existing distributed optimization algorithms applied to p…
Distributed OptimizationBALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization
In this paper, we propose a novel primal-dual proximal splitting algorithm (PD-PSA), named BALPA, for the composite optimization problem with equality constraints, where the loss function consists of a smooth term and a …
Distributed OptimizationContinual Learning with Distributed Optimization: Does CoCoA Forget?
We focus on the continual learning problem where the tasks arrive sequentially and the aim is to perform well on the newly arrived task without performance degradation on the previously seen tasks. In contrast to the con…
Continual LearningDistributed OptimizationSimple and Scalable Algorithms for Cluster-Aware Precision Medicine
AI-enabled precision medicine promises a transformational improvement in healthcare outcomes by enabling data-driven personalized diagnosis, prognosis, and treatment. However, the well-known "curse of dimensionality" and…
ClusteringDistributed OptimizationPrognosisDistributed Optimization with Quantized Gradient Descent
In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, nodes can only communicate with their nei…
Distributed OptimizationImpact of Redundancy on Resilience in Distributed Optimization and Learning
This report considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost f…
Distributed Optimization