Security and Privacy Threats to Federated Learning: Issues, Methods, and Challenges Article Swipe
Junpeng Zhang
,
Hui Zhu
,
Fengwei Wang
,
Jiaqi Zhao
,
Qi Xu
,
Hui Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/2886795
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/2886795
Federated learning (FL) has nourished a promising method for data silos, which enables multiple participants to construct a joint model collaboratively without centralizing data. The security and privacy considerations of FL are focused on ensuring the robustness of the global model and the privacy of participants’ information. However, the FL paradigm is under various security threats from the adversary aggregator and participants. Therefore, it is necessary to comprehensively identify and classify potential threats to provide a theoretical basis for FL with security guarantees. In this paper, a unique classification of attacks, which reviews state-of-the-art research on security and privacy issues for FL, is constructed from the perspective of malicious threats based on different computing parties. Specifically, we categorize attacks with respect to performed by aggregator and participant, highlighting the Deep Gradients Leakage attacks and Generative Adversarial Networks attacks. Following an overview of attack methods, we discuss the primary mitigation techniques against security risks and privacy breaches, especially the application of blockchain and Trusted Execution Environments. Finally, several promising directions for future research are discussed.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/2886795
- https://downloads.hindawi.com/journals/scn/2022/2886795.pdf
- OA Status
- hybrid
- Cited By
- 68
- References
- 120
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4297521542Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/2886795Digital Object Identifier
- Title
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Security and Privacy Threats to Federated Learning: Issues, Methods, and ChallengesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-09-28Full publication date if available
- Authors
-
Junpeng Zhang, Hui Zhu, Fengwei Wang, Jiaqi Zhao, Qi Xu, Hui LiList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/2886795Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/scn/2022/2886795.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/scn/2022/2886795.pdfDirect OA link when available
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Computer science, Computer security, Adversary, Adversarial system, News aggregator, Threat model, Construct (python library), Robustness (evolution), Internet privacy, Artificial intelligence, World Wide Web, Chemistry, Biochemistry, Programming language, GeneTop concepts (fields/topics) attached by OpenAlex
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68Total citation count in OpenAlex
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2025: 21, 2024: 30, 2023: 15, 2022: 2Per-year citation counts (last 5 years)
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120Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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