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New FedRCD algorithms enable privacy-preserving federated causal discovery

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new family of algorithms called FedRCD for federated causal discovery in linear non-Gaussian acyclic models (LiNGAM). This approach addresses privacy concerns, particularly those related to regulations like GDPR, by enabling causal discovery without centralizing sensitive data. The FedRCD algorithms leverage higher-order cumulant tensors to overcome limitations of existing federated methods, such as FedISHC, which can fail under certain noise conditions. The proposed variants offer trade-offs between communication rounds and accuracy, with some supporting exact unlearning for individual clients or observations. AI

IMPACT Introduces privacy-preserving methods for causal discovery, potentially enabling more widespread application in regulated domains.

RANK_REASON The cluster describes a new family of algorithms presented in an academic paper, focusing on a novel research contribution in the field of causal discovery.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New FedRCD algorithms enable privacy-preserving federated causal discovery

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The cluster describes a new family of algorithms presented in an academic paper, focusing on a novel research contribution in the field of causal discovery.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Pablo Torrijos, Fabio Stella, Jos\'e A. G\'amez, Jos\'e M. Puerta ·

    Federated Causal Discovery via Regression-Directed Cumulants

    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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Federated Causal Discovery via Regression-Directed Cumulants

    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 sample size by centralising data from different clie…

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