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Research map / Optimization / Distributed and compressed

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication

ICML 2019 · Optimization › Distributed and compressed · approach: Communication complexity theory · 177 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use error-feedback compressed gossip with stored estimatesChoco-Gossip · Average consensus with arbitrary compression qualitylinear convergence rate: (1 - \dfrac{\delta^2 \omega}{82})^tHelped
Use qsgd_256 compressed communication (ablation)Choco-Gossip · Average consensus, ring topologyconsensus errorNo clear effect
Use rand_1% sparsification (ablation)Choco-Gossip · Average consensus, ring topologyiterations to converge: 100× slowerHurt
Use rand_1% sparsification (ablation)Choco-Gossip · Average consensus, ring topologycommunication cost to convergeHelped
Use rand_1% sparsification (ablation)Choco-SGD · epsilon and rcv1, sorted data, ring, n=9communication cost: 100× less communicationHelped
Use qsgd_4 quantization (ablation)Choco-SGD · epsilon and rcv1, sorted data, ring, n=9communication cost: approximately 15× less communicationHelped
Replace rand_1% sparsification with top_1% sparsification (ablation)Choco-SGD · epsilon and rcv1, sorted data, ring, n=9suboptimalityHelped
Use Choco-SGD instead of DCD-SGDDecentralized SGD · All reported decentralized SGD settingssuboptimalityHelped
Increase the quantization levels s (ablation)DCD-SGD · Decentralized SGD, qsgd_ssuboptimalityHelped
Use ECD-SGD instead of DCD-SGDDecentralized SGD · Reported decentralized SGD settingssuboptimalityHurt
Use exact averaging with a fully connected graph (ablation)Plain decentralized SGD · Decentralized SGD, fully connected topologysuboptimalityHelped

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