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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 251–275 of 536 papers

TitleStatusHype
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
Distributed Optimization with Quantized Gradient Descent—0
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance—0
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
Fractional Order Distributed Optimization—0
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges—0
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton—0
Fundamental Resource Trade-offs for Encoded Distributed Optimization—0
Generalized Gradient Descent is a Hypergraph Functor—0
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes—0
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization—0
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems—0
GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange—0
Gradient-Consensus: Linearly Convergent Distributed Optimization Algorithm over Directed Graphs—0
Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization—0
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks—0
Gradient Sparsification for Communication-Efficient Distributed Optimization—0
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games—0
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