Pocket diagnosis: Secure federated learning against poisoning attack in the cloud
Open accessAbstract
Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e., Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this article, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, calledSFAP. Concretely,SFAPprovides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to resist DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency ofSFAPas well as its capability of resisting DI-level poisoning attacks.
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HE is very effective for FL privacy preservation because most operations required by FL systems, such as aggregation, model updates, and gradient calculations, can be performed without accessing raw data.
“This approach is very effective for FL privacy preservation as most of the operations required by the FL systems like aggregation, model updates, and gradient calculations can be performed without accessing the raw data [34,35,36,37,38].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [37]
Homomorphic encryption can be used to evaluate the local model securely.
“To solve this problem, [38], [49], [51] used homomorphic encryption to evaluate the local model securely.”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [49]
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- Authors
- Ma, Z., Ma, J., Miao, Y., Liu, X., Choo, K.-K. R., & Deng, R. H.
- Affiliations
- Xidian University (CN)
- Fuzhou University (CN)
- The University of Texas at San Antonio (US)
- Singapore Management University (SG)
- Venue
- IEEE Transactions on Services Computing
- Published
- 2022-11-01
- Volume
- 15
- Issue
- 6
- Pages
- 3429-3442
- ISSN
- 1939-1374 (ISSN-L)
- 2372-0204
- OpenAlex
- W3176224843
- Cited by
- 50
- References
- 40
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 11 · 2023: 15 · 2022: 4 · 2021: 3
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Services Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- China Scholarship Council
- Cloud Technology Endowed Professorship
- Shaanxi Science & Technology Coordination & Innovation Project (2016TZC-G-6-3)
- National Natural Science Foundation of China (62072361, 62072109, U1804263, 62072352)
- Key R&D Program of Shaanxi Province (2019ZDLGY12-04, 2020ZDLGY09-06)
- Fundamental Research Funds for the Central Universities (JB211505)
- Guangxi Key Laboratory of Cryptography and Information Security (GCIS201917)
- Guangxi Key Laboratory of Trusted Software (KX202028)
- CCF-Tencent Open Fund WeBank Special Funding (CCF-Webank RAGR 20200102)
- CCF-NSFOCUS Kunpeng Research Fund (CCF-NSFOCUS 20200004)
- Hong Kong Scholar Program (XJ2019038)
- Henan Key Laboratory of Network Cryptography Technology (LNCT2020-A06)
- Innovation Fund of Xidian University (YJS2114)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- federated learning, data poisoning, diagnosis, privacy preservation, random forest, data islands, cloud security, formal security analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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