Fundamental Limits of Decentralized Data Shuffling

@article{Wan2020FundamentalLO,
  title={Fundamental Limits of Decentralized Data Shuffling},
  author={Kai Wan and Daniela Tuninetti and Mingyue Ji and G. Caire and P. Piantanida},
  journal={IEEE Transactions on Information Theory},
  year={2020},
  volume={66},
  pages={3616-3637}
}
Data shuffling of training data among different computing nodes (workers) has been identified as a core element to improve the statistical performance of modern large-scale machine learning algorithms. Data shuffling is often considered as one of the most significant bottlenecks in such systems due to the heavy communication load. Under a master-worker architecture (where a master has access to the entire dataset and only communication between the master and the workers is allowed) coding has… Expand
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