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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 251–300 of 536 papers

TitleStatusHype
Competition-Based Resilience in Distributed Quadratic Optimization—0
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point—0
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds—0
Distributed Dual Quaternion Based Localization of Visual Sensor Networks—0
Optimal Methods for Convex Risk Averse Distributed Optimization—0
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms—0
Correlated quantization for distributed mean estimation and optimization—0
Distributed Methods with Absolute Compression and Error Compensation—0
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control—0
Multi-objective Distributed Optimization for Zonal Distribution System with Multi-Microgrids—0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Distributed saddle point problems for strongly concave-convex functions—0
Spatial Reuse in Dense Wireless Areas: A Cross-layer Optimization Approach via ADMM—0
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting—0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client SynchronizationCode0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
End-to-End Quality-of-Service Assurance with Autonomous Systems: 5G/6G Case Study—0
Distributed gradient-based optimization in the presence of dependent aperiodic communication—0
Distributed Learning of Generalized Linear Causal Networks—0
Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework—0
Distributed Random Reshuffling over Networks—0
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems—0
Communication-Efficient Distributed SGD with Compressed Sensing—0
Distributed Graph Learning with Smooth Data Priors—0
Collaborative Learning over Wireless Networks: An Introductory Overview—0
Variance Reduction in Deep Learning: More Momentum is All You Need—0
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning—0
A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections—0
Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Decentralized Feature-Distributed Optimization for Generalized Linear Models—0
Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach—0
Parallel Feedforward Compensation for Output Synchronization: Fully Distributed Control and Indefinite Laplacian—0
Acceleration in Distributed Optimization under Similarity—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning—0
Distributed Optimization of Graph Convolutional Network using Subgraph Variance—0
Distributed Privacy-Preserving Electric Vehicle Charging Control Based on Secret Sharing—0
KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey—0
Distributed Optimization using Heterogeneous Compute SystemsCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
Distributed Online Optimization with Byzantine Adversarial Agents—0
Toward Communication Efficient Adaptive Gradient Method—0
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees—0
On the Convergence of Decentralized Adaptive Gradient Methods—0
The Minimax Complexity of Distributed Optimization—0
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
Dynamic communication topologies for distributed heuristics in energy system optimization algorithmsCode0
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