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Challenges and future directions of secure federated learning: a survey

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Zhang, Kaiyue, Song, Xuan, Zhang, Chenhan, Yu, Shui·2022·Frontiers of Computer Science

DOI 10.1007/s11704-021-0598-zPMC PMC8663756

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Abstract

Federated learning came into being with the increasing concern of privacy security, as people's sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users' raw data, but aggregates model parameters from each client and therefore protects user's privacy. Nonetheless, due to the inherent distributed nature of federated learning, it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server. In addition, some recent studies have shown that attackers can recover information merely from parameters. Hence, there is still lots of room to improve the current federated learning frameworks. In this survey, we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning. Several open issues and existing solutions in federated learning are discussed. We also point out the future research directions of federated learning.Electronic supplementary material Supplementary material is available in the online version of this article at 10.1007/s11704-021-0598-z.

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An innovative technological infrastructure for managing SARS-CoV-2 data across different cohorts in compliance with General Data Protection Regulation
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Retracted
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Tao, W., & Yin, Q. (2022) · IEEE International Conference on Big Data Analytics (ICBDA) · doi:10.1109/icbda55095.2022.9760330 · Cited by 1
Introduction to Federated Learning
Yu, S., & Cui, L. (2022) · Digital Privacy and Security · doi:10.1007/978-981-19-8692-5_1 · Cited by 1
Self-citation
Gradient Information from Google GBoard NWP LSTM is Sufficient to Reconstruct Words Typed
Suliman, M., & Leith, D. J. (2022) · Cyber Research Conference - Ireland (Cyber-RCI) · doi:10.1109/cyber-rci55324.2022.10032666
Mobile Keyword Prediction using Federated Learning
Chouhan, K. U., Jha, N. P. K., Jha, R. S., Kamaluddin, S. I., & Giri, N. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.50826

Authors: Zhang, K., Song, X., Zhang, C., & Yu, S.
Affiliations: University of Technology Sydney (AU)
Southern University of Science and Technology (CN)
Corresponding author: Song, Xuan
Yu, Shui
ORCID: Zhang, Kaiyue
Song, Xuan
Zhang, Chenhan
Yu, Shui
Venue: Frontiers of Computer Science
Publisher: Springer Science and Business Media LLC
Published: 2021-12-10
Volume: 16
Issue: 5
Pages: 165817-165817
ISSN: 2095-2228 (ISSN-L)
2095-2236
DOI: 10.1007/s11704-021-0598-z
PMC: PMC8663756
OpenAlex: W4200419653
Cited by: 127
References: 47
Citations by year: 2026: 14 · 2025: 26 · 2024: 43 · 2023: 29 · 2022: 14 · 2021: 1
FWCI: 1.56
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Frontiers of Computer Science (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
Keywords: federated learning, privacy preservation, secure federated learning, model parameter aggregation

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Challenges and future directions of secure federated learning: a survey

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