Autor según el artículo: Singh, AK; Blanco-Justicia, A; Domingo-Ferrer, J
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Blanco Justicia, Alberto / Domingo Ferrer, Josep
Palabras clave: Security Privacy Minorities Microaggregation Federated learning Fairness security privacy minorities fairness
Resumen: Reconciling machine learning with individual privacy is one of the main motivations behind federated learning (FL), a decentralized machine learning technique that aggregates partial models trained by clients on their own private data to obtain a global deep learning model. Even if FL provides stronger privacy guarantees to the participating clients than centralized learning collecting the clients’ data in a central server, FL is vulnerable to some attacks whereby malicious clients submit bad updates in order to prevent the model from converging or, more subtly, to introduce artificial bias in the classification (poisoning). Poisoning detection techniques compute statistics on the updates to identify malicious clients. A downside of anti-poisoning techniques is that they might lead to discriminate minority groups whose data are significantly and legitimately different from those of the majority of clients. This would not only be unfair, but would yield poorer models that would fail to capture the knowledge in the training data, especially when data are not independent and identically distributed (non-i.i.d.). In this work, we strive to strike a balance between fighting poisoning and accommodating diversity to help learning fairer and less discriminatory federated learning models. In this way, we forestall the exclusion of diverse clients while still ensuring detection of poisoning attacks. Empirical work on three data sets shows that employing our approach to tell legitimate from malicious updates produces models that are more accurate than those obtained with state-of-the-art poisoning detection techniques. Additionally, we explore the impact of our proposal on the performance of models on non-i.i.d local training data.
Áreas temáticas: Information systems Engenharias iv Engenharias iii Computer science, information systems Computer science, artificial intelligence Computer science applications Computer networks and communications Ciências biológicas i Ciência da computação
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: alberto.blanco@urv.cat josep.domingo@urv.cat
Identificador del autor: 0000-0002-1108-8082 0000-0001-7213-4962
Fecha de alta del registro: 2024-04-13
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://link.springer.com/article/10.1007/s10618-022-00912-6
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Data Mining And Knowledge Discovery. 37 (5): 1998-2023
Referencia de l'ítem segons les normes APA: Singh, AK; Blanco-Justicia, A; Domingo-Ferrer, J (2023). Fair detection of poisoning attacks in federated learning on non-i.i.d. data. Data Mining And Knowledge Discovery, 37(5), 1998-2023. DOI: 10.1007/s10618-022-00912-6
DOI del artículo: 10.1007/s10618-022-00912-6
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2023
Tipo de publicación: Journal Publications
Repositori URV