Truncated Non-Uniform Quantization for Distributed SGD
Guangfeng Yan Tan Li Yuanzhang Xiao Congduan Li Linqi Song
To address the communication bottleneck challenge in distributed learning,
our work introduces a novel two-stage quantization strategy designed to enhance
the communication efficiency of distributed Stochastic Gradient Descent (SGD).
The proposed method initially employs truncation to mitigate the impact of
long-tail noise, followed by a non-uniform quantization of the post-truncation
gradients based on their statistical characteristics. We provide a
comprehensive convergence analysis of the quantized distributed SGD,
establishing theoretical guarantees for its performance. Furthermore, by
minimizing the convergence error, we derive optimal closed-form solutions for
the truncation threshold and non-uniform quantization levels under given
communication constraints. Both theoretical insights and extensive experimental
evaluations demonstrate that our proposed algorithm outperforms existing
quantization schemes, striking a superior balance between communication
efficiency and convergence performance.