English(EN) Federated stochastic bilevel optimization with fully first-order gradients

新算法使用一阶梯度简化联邦双层优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一种新的联邦随机双层优化算法，该算法避免了计算成本高昂的二阶Hessian和Jacobian矩阵。这种新颖的方法，称为联邦随机方差缩减双层梯度下降，完全依赖于一阶预言机，显著缩短了运行时间。该算法还采用了一种独特的常数单时间尺度学习率机制进行变量更新，并包含了一种建立收敛速率的新策略。实验结果据称已证实了该算法的有效性。 AI

影响这项研究通过减少计算开销，可能导致更有效的复杂机器学习模型训练。

排序理由该集群包含一篇详细介绍机器学习问题新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yihan Zhang, Rohit Dhaipule, Chiu C Tan, Haibin Ling, Hongchang Gao · 2026-09-16 04:00

具有全一阶梯度的联邦随机双层优化

arXiv:2609.16350v1 Announce Type: new Abstract: Federated stochastic bilevel optimization has been actively studied in recent years due to its widespread applications in machine learning. However, most existing federated stochastic bilevel optimization algorithms require the comp…

本文如何被排名

报道来源 [1]

具有全一阶梯度的联邦随机双层优化

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