• Corpus ID: 246430634

BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression

@article{Zhao2022BEERFO,
  title={BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression},
  author={Haoyu Zhao and Boyue Li and Zhize Li and Peter Richt'arik and Yuejie Chi},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.13320}
}
Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allowed to communicate a small amount of quantized information (aka bits) with their neighbors over a predefined graph topology. Despite significant… 

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