Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning

• URL: http://arxiv.org/abs/2402.01798v1
• Date: Fri, 2 Feb 2024 06:14:31 GMT
• Title: Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning
• Authors: Guangfeng Yan, Tan Li, Yuanzhang Xiao, Hanxu Hou and Linqi Song
• Abstract summary: It is observed that gradient distributions are heavy-tailed, with outliers significantly influencing the design of compression strategies. Existing parameter quantization methods experience performance degradation when this heavy-tailed feature is ignored. We introduce a novel compression scheme specifically engineered for heavy-tailed gradient gradients, which effectively combines truncation with quantization.
• Score: 20.91559450517002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gradient compression has surfaced as a key technique to address the challenge of communication efficiency in distributed learning. In distributed deep learning, however, it is observed that gradient distributions are heavy-tailed, with outliers significantly influencing the design of compression strategies. Existing parameter quantization methods experience performance degradation when this heavy-tailed feature is ignored. In this paper, we introduce a novel compression scheme specifically engineered for heavy-tailed gradients, which effectively combines gradient truncation with quantization. This scheme is adeptly implemented within a communication-limited distributed Stochastic Gradient Descent (SGD) framework. We consider a general family of heavy-tail gradients that follow a power-law distribution, we aim to minimize the error resulting from quantization, thereby determining optimal values for two critical parameters: the truncation threshold and the quantization density. We provide a theoretical analysis on the convergence error bound under both uniform and non-uniform quantization scenarios. Comparative experiments with other benchmarks demonstrate the effectiveness of our proposed method in managing the heavy-tailed gradients in a distributed learning environment.

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