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Challenges and future directions of secure federated learning: a survey
Open accessAbstract
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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Metadata
- 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
- Venue
- Frontiers of Computer Science
- Published
- 2021-12-10
- Volume
- 16
- Issue
- 5
- Pages
- 165817-165817
- ISSN
- 2095-2228 (ISSN-L)
- 2095-2236
- 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
- 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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