English(EN) QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning

新的QSplitFL框架优化联邦学习分层点

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-10 04:00

研究人员开发了QSplitFL，一个使用深度Q学习优化联邦学习中分层点的新框架。与之前关注模型权重的旧方法不同，该方法考虑了客户端硬件能力，如CPU使用率和内存。QSplitFL旨在通过在各种数据集和模型架构上进行的实验证明，提高具有异构设备的联邦学习场景下的收敛速度和准确性。 AI

影响引入了一种优化联邦学习的新方法，有望提高异构设备的效率和准确性。

排序理由这是一篇详细介绍优化联邦学习新方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Nazmus Shakib Shadin, Xinyue Zhang, Jingyi Wang, Miao Pan · 2026-06-10 04:00

QSplitFL：能力感知深度Q学习用于Split Federated Learning中的最优分割点选择

arXiv:2606.09869v1 Announce Type: cross Abstract: Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost. However, determinin…