The Geometry of Privacy: A Two-Stage Analysis of Generative Membership Inference in Federated Learning

We study Membership Inference Attack (MIA) risk in Federated Learning through a two-stage lens that separates (i) whether a target client’s contribution is detectable after aggregation and system noise (Stage I: Signal Survival) from (ii) whether a surviving contribution induces a generative members...

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Auteurs principaux:	Borja Arroyo Galende, Patricia A. Apellániz, Alejandro Almodóvar, Silvia Uribe, Federico Álvarez, Juan Parras
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2026-05-01
Collection:	Big Data and Cognitive Computing
Sujets:	federated learning Membership Inference Attack generative models privacy synthetic data
Accès en ligne:	https://www.mdpi.com/2504-2289/10/5/163
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