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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
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
Distributed System Identification for Linear Stochastic Systems with Binary Sensors—0
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time—0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression—0
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition—0
BAGUA: Scaling up Distributed Learning with System RelaxationsCode1
Robust Distributed Optimization With Randomly Corrupted Gradients—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Asynchronous Stochastic Optimization Robust to Arbitrary Delays—0
Secure Distributed Training at ScaleCode1
DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain DecompositionCode1
Learning Autonomy in Management of Wireless Random Networks—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
Assessing the Impacts of Nonideal Communications on Distributed Optimal Power Flow Algorithms—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
Optimization in Open Networks via Dual Averaging—0
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