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Title: Achieving security and privacy in federated learning systems: Survey, research challenges and future directions
Author: Blanco Justicia, Alberto
Domingo Ferrer, Josep
Martínez Lluís, Sergio
Sánchez Ruenes, David
Flanagan, Adrian
Tan, Kuan Eik
Others: Universitat Oberta de Catalunya (UOC)
Universitat Rovira i Virgili
Huawei Technologies
Keywords: federated learning
machine learning
Issue Date: 17-Sep-2021
Publisher: Engineering Applications of Artificial Intelligence
Citation: Blanco-Justicia, A. [Alberto], Domingo Ferrer, J. [Josep], Martínez, S. [Sergio], Sánchez Ruenes, D. [David], Flanagan, A. [Adrian] & Tan, K.E. [Kuan Eeik]. (2021). Achieving security and privacy in federated learning systems: Survey, research challenges and future directions. Engineering Applications of Artificial Intelligence, 106(), 1-14. doi: 10.1016/j.engappai.2021.104468
Project identifier: info:eu-repo/grantAgreement/YBN2019035188
info:eu-repo/grantAgreement/2017 SGR 705
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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.
Language: English
ISSN: 0952-1976MIAR
Appears in Collections:Articles

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