English(EN) Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity

新框架改进了移动设备上MoE LLM的联邦学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了FedAlign-MoE，一个旨在改进移动边缘设备上混合专家（MoE）大语言模型的联邦学习框架。该方法解决了跨设备数据异构性带来的挑战，这种异构性可能导致门控偏好分歧和专家语义模糊。FedAlign-MoE通过加权和正则化确保路由一致性，同时对齐专家语义以保持专业化，并在非独立同分布（non-IID）环境中提高整体准确性和收敛速度。 AI

影响增强了在移动设备上直接训练大语言模型的可行性，提高了边缘AI应用的隐私性和效率。

排序理由详细介绍MoE LLM联邦学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zihan Fang, Qianru Wang, Haonan An, Zheng Lin, Yiqin Deng, Symeon Chatzinotas, Yuguang Fang · 2026-10-07 04:00

数据异构环境下移动边缘网络的联邦混合专家模型对齐

arXiv:2603.21276v2 Announce Type: replace-cross Abstract: The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE) architectures, which scale model capacity with limited computation. Since fine…

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报道来源 [1]

数据异构环境下移动边缘网络的联邦混合专家模型对齐

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