arXiv:2512.01708v1 Announce Type: new 
Abstract: Learning the structure of a Bayesian network from decentralized data poses two major challenges: (i) ensuring rigorous privacy guarantees for participants, and (ii) avoiding communication costs that scale poorly with dimensionality. In this work, we introduce Fed-Sparse-BNSL, a novel federated method for learning linear Gaussian Bayesian network structures that addresses both challenges. By combining differential privacy with greedy updates that target only a few relevant edges per participant, Fed-Sparse-BNSL efficiently uses the privacy budget while keeping communication costs low. Our careful algorithmic design preserves model identifiability and enables accurate structure estimation. Experiments on synthetic and real datasets demonstrate that Fed-Sparse-BNSL achieves utility close to non-private baselines while offering substantially stronger privacy and communication efficiency.

تم تقديم طريقة فدرالية جديدة تُدعى Fed-Sparse-BNSL لتعلم هياكل الشبكات البايزية الغاوسية الخطية من بيانات موزعة، مما يعالج تحديات الخصوصية وكفاءة الاتصال. تجمع هذه الطريقة بين الخصوصية التفاضلية والتحديثات المستهدفة لتحسين الأداء مع الحفاظ على ضمانات خصوصية صارمة.

Se ha introducido un nuevo método federado llamado Fed-Sparse-BNSL para aprender estructuras de redes bayesianas gaussianas lineales a partir de datos descentralizados, abordando los desafíos de privacidad y eficiencia de comunicación. Este método combina la privacidad diferencial con actualizaciones dirigidas para mejorar el rendimiento mientras mantiene garantías de privacidad rigurosas.

Une nouvelle méthode fédérée appelée Fed-Sparse-BNSL a été introduite pour apprendre des structures de réseaux bayésiens gaussiens linéaires à partir de données décentralisées, abordant les défis de la confidentialité et de l'efficacité de la communication. Cette méthode combine la confidentialité différentielle avec des mises à jour ciblées pour améliorer les performances tout en maintenant des garanties de confidentialité rigoureuses.

A novel federated method called Fed-Sparse-BNSL has been introduced for learning linear Gaussian Bayesian network structures from decentralized data, addressing privacy and communication efficiency challenges. This method combines differential privacy with targeted updates to enhance performance while maintaining rigorous privacy guarantees.

Differentially Private and Federated Structure Learning in Bayesian Networks

arXiv:2512.06154v1 Announce Type: new 
Abstract: Learning invariant graph representations for out-of-distribution (OOD) generalization remains challenging because the learned representations often retain spurious components. To address this challenge, this work introduces a new tool from information theory called Partial Information Decomposition (PID) that goes beyond classical information-theoretic measures. We identify limitations in existing approaches for invariant representation learning that solely rely on classical information-theoretic measures, motivating the need to precisely focus on redundant information about the target $Y$ shared between spurious subgraphs $G_s$ and invariant subgraphs $G_c$ obtained via PID. Next, we propose a new multi-level optimization framework that we call -- Redundancy-guided Invariant Graph learning (RIG) -- that maximizes redundant information while isolating spurious and causal subgraphs, enabling OOD generalization under diverse distribution shifts. Our approach relies on alternating between estimating a lower bound of redundant information (which itself requires an optimization) and maximizing it along with additional objectives. Experiments on both synthetic and real-world graph datasets demonstrate the generalization capabilities of our proposed RIG framework.

تقدم دراسة جديدة إطارًا يسمى التعلم البياني invariant-guided بواسطة المعلومات الزائدة (RIG)، والذي يستخدم تحليل المعلومات الجزئية (PID) لتحسين التعميم خارج التوزيع (OOD) في تعلم تمثيلات الرسوم البيانية. تهدف هذه الطريقة إلى التخفيف من احتفاظ التمثيلات المتعلمة بالمكونات الزائفة من خلال تعظيم المعلومات الزائدة مع عزل المجموعات السببية.

Un nuevo estudio presenta un marco denominado aprendizaje gráfico invariante guiado por la redundancia (RIG), que utiliza la descomposición de información parcial (PID) para mejorar la generalización fuera de distribución (OOD) en el aprendizaje de representaciones gráficas. Este enfoque busca mitigar la retención de componentes espurios en las representaciones aprendidas al maximizar la información redundante mientras aísla los subgrupos causales.

Une nouvelle étude présente un cadre appelé apprentissage graphique invariant guidé par la redondance (RIG), qui utilise la décomposition d'information partielle (PID) pour améliorer la généralisation hors distribution (OOD) dans l'apprentissage des représentations graphiques. Cette approche vise à atténuer la rétention de composants fallacieux dans les représentations apprises en maximisant l'information redondante tout en isolant les sous-graphes causaux.

A new study introduces a framework called Redundancy-guided Invariant Graph learning (RIG), which utilizes Partial Information Decomposition (PID) to enhance out-of-distribution (OOD) generalization in graph representation learning. This approach aims to mitigate the retention of spurious components in learned representations by maximizing redundant information while isolating causal subgraphs.

Differentially Private and Federated Structure Learning in Bayesian Networks

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