Autor segons l'article: Blanco-Justicia A; Domingo-Ferrer J; Martínez S; Sánchez D; Flanagan A; Tan KE
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Blanco Justicia, Alberto / Domingo Ferrer, Josep / Martinez Lluis, Sergio / Sánchez Ruenes, David
Paraules clau: Self-enforcing protocols Security Privacy Machine learning Federated learning security privacy machine learning
Resum: 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.
Àrees temàtiques: 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
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: sergio.martinezl@urv.cat alberto.blanco@urv.cat josep.domingo@urv.cat david.sanchez@urv.cat
Identificador de l'autor: 0000-0002-3941-5348 0000-0002-1108-8082 0000-0001-7213-4962 0000-0001-7275-7887
Data d'alta del registre: 2023-02-23
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/pii/S095219762100316X?via%3Dihub
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Engineering Applications Of Artificial Intelligence. 106
Referència de l'ítem segons les normes APA: Blanco-Justicia A; Domingo-Ferrer J; Martínez S; Sánchez D; Flanagan A; Tan KE (2021). Achieving security and privacy in federated learning systems: Survey, research challenges and future directions. Engineering Applications Of Artificial Intelligence, 106(), -. DOI: 10.1016/j.engappai.2021.104468
DOI de l'article: 10.1016/j.engappai.2021.104468
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2021
Tipus de publicació: Journal Publications
Repositori URV