Corpus ID: 220962352

# PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning

@article{Vogels2020PowerGossipPL,
title={PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning},
author={Thijs Vogels and Sai Praneeth Reddy Karimireddy and Martin Jaggi},
journal={ArXiv},
year={2020},
volume={abs/2008.01425}
}
• Published 2020
• Computer Science, Mathematics
• ArXiv
Lossy gradient compression has become a practical tool to overcome the communication bottleneck in centrally coordinated distributed training of machine learning models. However, algorithms for decentralized training with compressed communication over arbitrary connected networks have been more complicated, requiring additional memory and hyperparameters. We introduce a simple algorithm that directly compresses the model differences between neighboring workers using low-rank linear compressors… Expand
10 Citations

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#### References

SHOWING 1-10 OF 38 REFERENCES
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
• Computer Science
• ICML
• 2019
This work provides a detailed analysis on this two-pass communication model and its asynchronous parallel variant, with error-compensated compression both on the worker nodes and on the parameter server, and admits three very nice properties: it is compatible with an arbitrary compression technique, it admits an improved convergence rate and it admits linear speedup with respect to the number of workers. Expand
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
• Xiangru Lian, Wei Zhang
• Computer Science, Mathematics
• NIPS
• 2017
This paper studies a D-PSGD algorithm and provides the first theoretical analysis that indicates a regime in which decentralized algorithms might outperform centralized algorithms for distributed stochastic gradient descent. Expand
GradZip: Gradient compression using alternating matrix factorization for large-scale deep learning
• 2019
GradZip: Gradient compression using alternating matrix factorization for large-scale deep learning, 2019
• 2019
Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation
A comprehensive survey of the most influential compressed communication methods for DNN training, together with an intuitive classification (i.e., quantization, sparsification, hybrid and low-rank), and a unified framework and API that allows for consistent and easy implementation of compressed communication on popular machine learning toolkits are presented. Expand
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
• Computer Science, Mathematics
• NeurIPS
• 2019
A new low-rank gradient compressor based on power iteration that can compress gradients rapidly, efficiently aggregate the compressed gradients using all-reduce, and achieve test performance on par with SGD is proposed. Expand
Decentralized Deep Learning with Arbitrary Communication Compression
• Computer Science, Mathematics
• ICLR
• 2020
The use of communication compression in the decentralized training context achieves linear speedup in the number of workers and supports higher compression than previous state-of-the art methods. Expand
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
• Computer Science, Mathematics
• ICML
• 2019
This work presents a novel gossip-based stochastic gradient descent algorithm, CHOCO-SGD, that converges at rate $\mathcal{O}\left(1/(nT) + 1/(T \delta^2 \omega)^2\right)$ for strongly convex objectives, where $T$ denotes the number of iterations and $\delta$ the eigengap of the connectivity matrix. Expand
Advances and Open Problems in Federated Learning
• P. Kairouz, +55 authors Sen Zhao
• Computer Science, Mathematics
• Found. Trends Mach. Learn.
• 2021
Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges. Expand
Distributed stochastic gradient tracking methods
• Computer Science, Mathematics
• Math. Program.
• 2021
It is shown that when the network is well-connected, GSGT incurs lower communication cost than DSGT while maintaining a similar computational cost, which is a comparable performance to a centralized stochastic gradient algorithm. Expand