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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 331–340 of 536 papers

TitleStatusHype
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg—0
Federated Learning with Compression: Unified Analysis and Sharp Guarantees—0
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms—0
Federated Multi-Level Optimization over Decentralized Networks—0
Federated Optimization:Distributed Optimization Beyond the Datacenter—0
Federated Optimization: Distributed Machine Learning for On-Device Intelligence—0
Federated Optimization with Doubly Regularized Drift Correction—0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling—0
FedSplit: An algorithmic framework for fast federated optimization—0
Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping—0
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