Author, as appears in the article.: Blanco-Justicia, Alberto; Domingo-Ferrer, Josep; Martinez, Sergio; Sanchez, David; Flanagan, Adrian; Tan, Kuan Eeik
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Blanco Justicia, Alberto / Domingo Ferrer, Josep / Martinez Lluis, Sergio / Sánchez Ruenes, David
Keywords: Self-enforcing protocols Security Privacy Machine learning Federated learning security privacy machine learning
Abstract: 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.
Thematic Areas: 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
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: alberto.blanco@urv.cat david.sanchez@urv.cat sergio.martinezl@urv.cat josep.domingo@urv.cat
Author identifier: 0000-0002-1108-8082 0000-0001-7275-7887 0000-0002-3941-5348 0000-0001-7213-4962
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.sciencedirect.com/science/article/pii/S095219762100316X?via%3Dihub
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Engineering Applications Of Artificial Intelligence. 106 (2021): 104468-
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
Article's DOI: 10.1016/j.engappai.2021.104468
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Journal Publications
Repositori URV