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Distributed Optimization

The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.

Source: Analysis of Distributed StochasticDual Coordinate Ascent

Papers

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Showing 51–100 of 536 papers

TitleStatusHype
Communication-Efficient Distributed Kalman Filtering using ADMM—0
Acceleration in Distributed Optimization under Similarity—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
Communication-Efficient Distributed SGD with Compressed Sensing—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
A Survey on Distributed Evolutionary Computation—0
A survey on secure decentralized optimization and learning—0
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning—0
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids—0
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization—0
An Equivalent Circuit Approach to Distributed Optimization—0
Byzantine Fault Tolerant Distributed Linear Regression—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)—0
Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming—0
Communication-Efficient Accurate Statistical Estimation—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach—0
Adaptive Consensus ADMM for Distributed Optimization—0
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
Can Competition Outperform Collaboration? The Role of Misbehaving Agents—0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression—0
CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization—0
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence—0
Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach—0
Centralised and Distributed Optimization for Aggregated Flexibility Services Provision—0
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
Collaborative Learning over Wireless Networks: An Introductory Overview—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
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