English(EN) Federated Causal Discovery via Regression-Directed Cumulants

新的 FedRCD 算法支持隐私保护的因果发现

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

研究人员开发了一系列新的联邦因果发现算法，称为 FedRCD，旨在隐私保护环境下与线性非高斯无环模型 (LiNGAM) 协同工作。这些算法利用高阶累积量张量，无需集中敏感数据即可实现因果发现，解决了现有方法（如 FedISHC）在处理近对称噪声时遇到的局限性。FedRCD 提供了平衡通信轮次与准确性的变体，并支持精确的遗忘，在数据隐私至关重要的实际应用中展现出潜力。 AI

影响在联邦学习环境中实现更强大、更注重隐私的因果发现。

排序理由该集群包含一篇详细介绍因果发现新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Pablo Torrijos, Fabio Stella, Jos\'e A. G\'amez, Jos\'e M. Puerta · 2026-09-04 04:00

通过回归导向累积量进行联邦因果发现

arXiv:2609.03705v1 Announce Type: new Abstract: In this paper we study linear non-Gaussian acyclic models (LiNGAM) when used in federated environments. These causal models allow one to go beyond Markov equivalence. However, in many domains data are scarce, and increasing the samp…

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通过回归导向累积量进行联邦因果发现

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