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arXiv · 2506.17974

Trustworthy Efficient Communication for Distributed Learning using LQ-SGD Algorithm

Hongyang Li·Lincen Bai·Caesar Wu·Mohammed Chadli·Said Mammar·Pascal Bouvry
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Abstract

We propose LQ-SGD (Low-Rank Quantized Stochastic Gradient Descent), an efficient communication gradient compression algorithm designed for distributed training. LQ-SGD further develops on the basis of PowerSGD by incorporating the low-rank approximation and log-quantization techniques, which drastically reduce the communication overhead, while still ensuring the convergence speed of training and model accuracy. In addition, LQ-SGD and other compression-based methods show stronger resistance to gradient inversion than traditional SGD, providing a more robust and efficient optimization path for distributed learning systems.

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Hongyang Li, Lincen Bai, Caesar Wu, Mohammed Chadli, Said Mammar, Pascal Bouvry. 2025-06-22. Trustworthy Efficient Communication for Distributed Learning using LQ-SGD Algorithm. https://arxiv.org/abs/2506.17974

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