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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 231–240 of 536 papers

TitleStatusHype
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
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
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
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