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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 131–140 of 536 papers

TitleStatusHype
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions—0
LASER: Linear Compression in Wireless Distributed Optimization—0
Network-aware EV charging and discharging in unbalanced distribution grids: A distributed, robust approach against communication failures—0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization—0
Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem PrincipleCode0
Federated Multi-Level Optimization over Decentralized Networks—0
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization—0
Federated Conditional Stochastic Optimization—0
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