English(EN) Ampere: Communication-Efficient and High-Accuracy Split Federated Learning

Ampere系统提升分体式联邦学习的效率和准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员推出Ampere，一个旨在提高分体式联邦学习（SFL）效率和准确性的新系统。Ampere解决了传统SFL的局限性，传统SFL通常存在通信开销高和非独立同分布（non-IID）数据准确性降低的问题。新系统采用单向块间训练和轻量级辅助网络生成方法，显著降低了设备上的计算和设备-服务器通信。实验表明，与现有的SFL方法相比，Ampere的模型准确性提高了多达11.70个百分点，训练速度提高了18.6倍，通信和计算成本显著降低，并且在异构数据上表现出更好的性能。 AI

影响提高了联邦学习的效率和准确性，可能支持分布式设备上更复杂的模型。

排序理由详细介绍分体式联邦学习新系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zihan Zhang, Leon Wong, Blesson Varghese · 2026-08-31 04:00

Ampere：通信高效且高精度的分片联邦学习

arXiv:2507.07130v2 Announce Type: replace-cross Abstract: A Federated Learning (FL) system collaboratively trains neural networks across devices and a server but is limited by significant on-device computation costs. Split Federated Learning (SFL) systems mitigate this by offload…

本文如何被排名

报道来源 [1]

Ampere：通信高效且高精度的分片联邦学习

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