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English(EN) SplitLite: Low-Rank Residual Compression for Split Learning

SplitLite 方法大幅降低 LLM 微调通信成本

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 SplitLite，一种用于在设备上高效进行大型语言模型 (LLM) 联邦微调的新方法。该方法通过利用训练周期之间激活和梯度残差的低秩结构，解决了分层学习中的通信瓶颈。SplitLite 在不影响 GLUE 等基准测试的模型性能的情况下，实现了通信成本的大幅降低，激活上行链路降低高达 93.5%，总体降低 83.7%。 AI

影响 降低了设备上 LLM 微调的通信开销，有可能在资源受限的设备上实现更强大的模型。

排序理由 该集群包含一篇详细介绍 LLM 微调新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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SplitLite 方法大幅降低 LLM 微调通信成本

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该集群包含一篇详细介绍 LLM 微调新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tao Li, Yulin Tang, Qi Guo, Xianhao Chen ·

    SplitLite：用于拆分学习的低秩残差压缩

    arXiv:2608.23018v1 Announce Type: cross Abstract: Federated fine-tuning of on-device large language models (LLMs) faces a significant computing burden. To overcome this limitation, split learning (SL) has emerged as a promising solution, which offloads the primary training worklo…

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