DeepSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
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Communication is a key bottleneck in distributed training. Recently, an error-compensated compression technology was particularly designed for the centralized learning and receives huge successes, by showing significant advantages over state-of-the-art compression based methods in saving the communication cost. Since the decentralized training has been witnessed to be superior to the traditional centralized training in the communication restricted scenario, therefore a natural question to ask is "how to apply the error-compensated technology to the decentralized learning to further reduce the communication cost." However, a trivial extension of compression based centralized training algorithms does not exist for the decentralized scenario. key difference between centralized and decentralized training makes this extension extremely non-trivial. In this paper, we propose an elegant algorithmic design to employ error-compensated stochastic gradient descent for the decentralized scenario, named DeepSqueeze. Both the theoretical analysis and the empirical study are provided to show the proposed DeepSqueeze algorithm outperforms the existing compression based decentralized learning algorithms. To the best of our knowledge, this is the first time to apply the error-compensated compression to the decentralized learning.
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