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arXiv Machine Learning By Pablo Torrijos, Fabio Stella, Jos\'e A. G\'amez, Jos\'e M. Puerta

Federated Causal Discovery via Regression-Directed Cumulants

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The paper introduces FedRCD, a family of federated causal discovery algorithms for linear non‑Gaussian acyclic models (LiNGAM). FedRCD overcomes limitations of the existing FedISHC method, which fails under near‑symmetric noise, by leveraging higher‑order cumulant tensors that aggregate across independent client data in a single communication round. Three variants of FedRCD trade off communication rounds against algebraic noise, with two exact federated counterparts of centralised HC and HC‑LiNGAM, and a single‑round variant that supports exact unlearning at any granularity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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