Autor según el artículo: Blanco-Justicia, Alberto; Domingo-Ferrer, Josep; Martinez, Sergio; Sanchez, David; Flanagan, Adrian; Tan, Kuan Eeik
Departamento: Enginyeria Informàtica i Matemàtiques
Autor/es de la URV: Blanco Justicia, Alberto / Domingo Ferrer, Josep / Martinez Lluis, Sergio / Sánchez Ruenes, David
Palabras clave: Self-enforcing protocols Security Privacy Machine learning Federated learning security privacy machine learning
Resumen: Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients that privately store their own training data. In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server. However, FL is not free of issues. On the one hand, the model updates sent by the clients at each training epoch might leak information on the clients’ private data. On the other hand, the model learnt by the server may be subjected to attacks by malicious clients; these security attacks might poison the model or prevent it from converging. In this paper, we first examine security and privacy attacks to FL and critically survey solutions proposed in the literature to mitigate each attack. Afterwards, we discuss the difficulty of simultaneously achieving security and privacy protection. Finally, we sketch ways to tackle this open problem and attain both security and privacy.
Áreas temáticas: Robotics & automatic control Medicina i Materiais Matemática / probabilidade e estatística Linguística e literatura Interdisciplinar Engineering, multidisciplinary Engineering, electrical & electronic Engineering Engenharias iv Engenharias iii Engenharias ii Engenharias i Electrical and electronic engineering Control and systems engineering Computer science, artificial intelligence Ciências agrárias i Ciência de alimentos Ciência da computação Biotecnología Automation & control systems Artificial intelligence Administração pública e de empresas, ciências contábeis e turismo
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: alberto.blanco@urv.cat david.sanchez@urv.cat sergio.martinezl@urv.cat josep.domingo@urv.cat
Identificador del autor: 0000-0002-1108-8082 0000-0001-7275-7887 0000-0002-3941-5348 0000-0001-7213-4962
Fecha de alta del registro: 2024-10-12
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S095219762100316X?via%3Dihub
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Engineering Applications Of Artificial Intelligence. 106 (2021): 104468-
Referencia de l'ítem segons les normes APA: Blanco-Justicia, Alberto; Domingo-Ferrer, Josep; Martinez, Sergio; Sanchez, David; Flanagan, Adrian; Tan, Kuan Eeik (2021). Achieving security and privacy in federated learning systems: Survey, research challenges and future directions. Engineering Applications Of Artificial Intelligence, 106(2021), 104468-. DOI: 10.1016/j.engappai.2021.104468
DOI del artículo: 10.1016/j.engappai.2021.104468
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2021
Tipo de publicación: Journal Publications