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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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TitleStatusHype
Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching—0
DPLib: A Standard Benchmark Library for Distributed Power System Analysis and OptimizationCode1
Multi-Timescale Gradient Sliding for Distributed Optimization—0
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking—0
Distributed gradient methods under heavy-tailed communication noise—0
Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading—0
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations—0
Federated Learning: From Theory to Practice—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimization—0
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