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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 276–300 of 536 papers

TitleStatusHype
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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