Quantizing data for distributed learning

• URL: http://arxiv.org/abs/2012.07913v2
• Date: Wed, 24 Mar 2021 20:20:04 GMT
• Title: Quantizing data for distributed learning
• Authors: Osama A. Hanna, Yahya H. Ezzeldin, Christina Fragouli, Suhas Diggavi
• Abstract summary: We consider machine learning applications that train a model by leveraging data over a network, where communication constraints can create a performance bottleneck. A number of recent approaches propose to overcome this bottleneck through compression of updates, but as models become larger, so does the size of the dataset. In paper, we propose that quantizes data instead of over gradient updates and can support learning applications.
• Score: 24.46948464551684
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We consider machine learning applications that train a model by leveraging data distributed over a network, where communication constraints can create a performance bottleneck. A number of recent approaches propose to overcome this bottleneck through compression of gradient updates. However, as models become larger, so does the size of the gradient updates. In this paper, we propose an alternate approach, that quantizes data instead of gradients, and can support learning over applications where the size of gradient updates is prohibitive. Our approach combines aspects of: (1) sample selection; (2) dataset quantization; and (3) gradient compensation. We analyze the convergence of the proposed approach for smooth convex and non-convex objective functions and show that we can achieve order optimal convergence rates with communication that mostly depends on the data rather than the model (gradient) dimension. We use our proposed algorithm to train ResNet models on the CIFAR-10 and ImageNet datasets, and show that we can achieve an order of magnitude savings over gradient compression methods.

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