Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Distributed Optimization via Kernelized Multi-armed Bandits
Multi-armed bandit algorithms provide solutions for sequential decision-making where learning takes place by interacting with the environment. In this work, we model a distributed optimization problem as a multi-agent ke…
Decision MakingDistributed OptimizationMulti-Armed BanditsSequential Decision MakingFederated Learning Assisted Distributed Energy Optimization
The increased penetration of distributed energy resources and the adoption of sensing and control technologies are driving the transition from our current centralized electric grid to a distributed system controlled by m…
Decision MakingDistributed Optimizationenergy managementFederated Learning+1Leveraging Function Space Aggregation for Federated Learning at Scale
The federated learning paradigm has motivated the development of methods for aggregating multiple client updates into a global server model, without sharing client data. Many federated learning algorithms, including the …
Distributed OptimizationFederated LearningShort vs. Long-term Coordination of Drones: When Distributed Optimization Meets Deep Reinforcement Learning
Swarms of autonomous interactive drones can provide compelling sensing capabilities in Smart City applications, such as traffic monitoring. This paper focuses on the task assignment problem for large-scale spatio-tempora…
Decision MakingDeep Reinforcement LearningDisaster ResponseDistributed Optimization+3DiLoCo: Distributed Low-Communication Training of Language Models
Large language models (LLM) have become a critical component in many applications of machine learning. However, standard approaches to training LLM require a large number of tightly interconnected accelerators, with devi…
Distributed OptimizationDiscretized Distributed Optimization over Dynamic Digraphs
We consider a discrete-time model of continuous-time distributed optimization over dynamic directed-graphs (digraphs) with applications to distributed learning. Our optimization algorithm works over general strongly conn…
Distributed OptimizationOptimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow
The future power grid may rely on distributed optimization to determine the set-points for huge numbers of distributed energy resources. There has been significant work on applying distributed algorithms to optimal power…
Distributed OptimizationAsynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks
Distributed learning and adaptation have received significant interest and found wide-ranging applications in machine learning and signal processing. While various approaches, such as shared-memory optimization, multi-ta…
Distributed OptimizationFederated LearningMulti-Task LearningGoal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems
The proliferation of novel industrial applications at the wireless edge, such as smart grids and vehicle networks, demands the advancement of cyber-physical systems. The performance of CPSs is closely linked to the last-…
Decision MakingDistributed OptimizationFederated LearningEControl: Fast Distributed Optimization with Compression and Error Control
Modern distributed training relies heavily on communication compression to reduce the communication overhead. In this work, we study algorithms employing a popular class of contractive compressors in order to reduce comm…
Distributed OptimizationZeroth-Order Feedback-Based Optimization for Distributed Demand Response
Distributed demand response is a typical distributed optimization problem that requires coordination among multiple agents to satisfy demand response requirements. However, existing distributed algorithms for this proble…
Distributed OptimizationCorrelation Aware Sparsified Mean Estimation Using Random Projection
We study the problem of communication-efficient distributed vector mean estimation, a commonly used subroutine in distributed optimization and Federated Learning (FL). Rand-$k$ sparsification is a commonly used technique…
Distributed OptimizationFederated LearningMachine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets
Amid the increasing interest in the deployment of Distributed Energy Resources (DERs), the Virtual Power Plant (VPP) has emerged as a pivotal tool for aggregating diverse DERs and facilitating their participation in whol…
Distributed OptimizationDiversityDistributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation
This paper proposes two nonlinear dynamics to solve constrained distributed optimization problem for resource allocation over a multi-agent network. In this setup, coupling constraint refers to resource-demand balance wh…
AllCPUDistributed OptimizationQuantization+1Distributed Linear Regression with Compositional Covariates
With the availability of extraordinarily huge data sets, solving the problems of distributed statistical methodology and computing for such data sets has become increasingly crucial in the big data area. In this paper, w…
Distributed OptimizationregressionDistributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions
This paper studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of…
Distributed OptimizationDetecting Shared Data Manipulation in Distributed Optimization Algorithms
This paper investigates the vulnerability of the Alternating Direction Method of Multipliers (ADMM) algorithm to shared data manipulation, with a focus on solving optimal power flow (OPF) problems. Deliberate data manipu…
Bilevel OptimizationDistributed OptimizationLASER: Linear Compression in Wireless Distributed Optimization
Data-parallel SGD is the de facto algorithm for distributed optimization, especially for large scale machine learning. Despite its merits, communication bottleneck is one of its persistent issues. Most compression scheme…
Distributed OptimizationLanguage ModelingLanguage ModellingNetwork-aware EV charging and discharging in unbalanced distribution grids: A distributed, robust approach against communication failures
This paper proposes a distributed optimization-based algorithm for electric vehicle (EV) charging and discharging, incorporating EV customer economics and distribution network constraints enforced on an unbalanced distri…
Distributed OptimizationCommunication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Byzantine robustness is an essential feature of algorithms for certain distributed optimization problems, typically encountered in collaborative/federated learning. These problems are usually huge-scale, implying that co…
Distributed OptimizationFederated Learning