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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 151–175 of 536 papers

TitleStatusHype
Distributed Averaging Methods for Randomized Second Order Optimization—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks—0
Byzantine Fault Tolerant Distributed Linear Regression—0
An Equivalent Circuit Approach to Distributed Optimization—0
Discretized Distributed Optimization over Dynamic Digraphs—0
Diffusion Adaptation over Networks—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints—0
Differentially Private Distributed Convex Optimization—0
Differentially Private Consensus-Based Distributed Optimization—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
DiLoCo: Distributed Low-Communication Training of Language Models—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
Distributed and time-varying primal-dual dynamics via contraction analysis—0
Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent—0
Design of heterogeneous multi-agent system for distributed computation—0
Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach—0
Distributed Convex Optimization With Limited Communications—0
Distributed Convex Optimization with State-Dependent (Social) Interactions over Random Networks—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
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