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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 161–170 of 536 papers

TitleStatusHype
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization—0
Federated Multi-Level Optimization over Decentralized Networks—0
Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem PrincipleCode0
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization—0
Federated Conditional Stochastic Optimization—0
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise—0
A primal-dual perspective for distributed TD-learningCode0
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks—0
On Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality—0
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