English(EN) FedSubMuon: Communication-Efficient Federated LLM Fine-Tuning via Structured Subspace Muon

新的FedSubMuon方法大幅降低LLM联邦微调的通信成本

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了FedSubMuon，一种用于大型语言模型（LLM）联邦微调的新颖方法，可显著降低通信成本。该方法优化了共享结构化子空间内的紧凑系数矩阵，从而在保持矩阵感知优化的优势的同时实现高效更新。一个扩展版本FedSubMuon-GT通过使用投影梯度自适应跟踪的子空间基，进一步提高了准确性。实验表明，FedSubMuon-GT在多个数据集-模型对上取得了卓越的准确性，而FedSubMuon在各种通信预算下提供了最佳性能，大幅超越了基线。 AI

影响降低了联邦LLM训练中的通信开销，可能使跨设备模型适应更加高效。

排序理由该条目是一篇学术论文，详细介绍了一种新的LLM微调方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Shaolong Chen, Youming Tao, Shuzhen Chen, Falko Dressler, Qingqing Ye, Di Wang · 2026-09-10 04:00

FedSubMuon: 通过结构化子空间Muon实现通信高效的联邦LLM微调

arXiv:2609.06073v1 Announce Type: cross Abstract: Federated fine-tuning adapts large language models (LLMs) to decentralized client data, but its scalability in cross-device training is often limited by the high communication cost. Muon is an optimizer that improves optimization …

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报道来源 [1]

FedSubMuon: 通过结构化子空间Muon实现通信高效的联邦LLM微调

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