The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Blockchain-based fairness-enhanced federated learning scheme against label flipping attack
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Through GAN attacks, adversaries can also complete data category inferences (Liu et al. 2023b) and label inferences (Jin et al. 2023).”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Jin et al. 2023
Citations
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning
Ma, Z., Ma, J., Miao, Y., Li, Y., & Deng, R. H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3169918 · Cited by 371
Self-citationDataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3162397 · Cited by 362
Self-citationPrivacy-Enhanced Federated Learning Against Poisoning Adversaries
Liu, X., Li, H., Xu, G., Chen, Z., Huang, X., & Lu, R. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3108434 · Cited by 351
Privacy-preserving blockchain-based federated learning for traffic flow prediction
Qi, Y., Hossain, M. S., Nie, J., & Li, X. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.12.003 · Cited by 339
A Secure Federated Learning Framework for 5G Networks
Liu, Y., Peng, J., Kang, J., Iliyasu, A. M., Niyato, D., & El-Latif, A. A. A. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.01.1900525 · Cited by 290
A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems
Arachchige, P. C. M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., & Atiquzzaman, M. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2974555 · Cited by 236
Research works printed but not linked: 18
- Qiu, 2018, Privacy-preserving wireless communications using bipartite matching in social big data, Future Gener Comput Syst, 87, 772, 10.1016/j.future.2017.08.004 · DOI
- Gai, 2017, Blend arithmetic operations on tensor-based fully homomorphic encryption over real numbers, IEEE Trans Ind Inf, 14, 3590, 10.1109/TII.2017.2780885 · DOI
- Qiu, 2020, Selective encryption on ECG data in body sensor network based on supervised machine learning, Inf Fusion, 55, 59, 10.1016/j.inffus.2019.07.012 · DOI
- Li, 2020, Federated learning: Challenges, methods, and future directions, IEEE Signal Process Mag, 37, 50, 10.1109/MSP.2020.2975749 · DOI · Paper page
- Kalapaaking, 2022, Blockchain-based federated learning with secure aggregation in trusted execution environment for internet-of-things, IEEE Trans Ind Inf, 19, 1703, 10.1109/TII.2022.3170348 · DOI
- Gai, 2019, Differential privacy-based blockchain for industrial internet-of-things, IEEE Trans Ind Inf, 16, 4156, 10.1109/TII.2019.2948094 · DOI
- Xu, 2023, BESIFL: Blockchain-empowered secure and incentive federated learning paradigm in IoT, IEEE Internet Things J, 10, 6561, 10.1109/JIOT.2021.3138693 · DOI
- Li, 2020, Crowdsfl: A secure crowd computing framework based on blockchain and federated learning, Electronics, 9, 773, 10.3390/electronics9050773 · DOI
- Warnat-Herresthal, 2021, Swarm learning for decentralized and confidential clinical machine learning, Nature, 594, 265, 10.1038/s41586-021-03583-3 · DOI · Paper page
- Li, 2022, Multitentacle federated learning over software-defined industrial internet of things against adaptive poisoning attacks, IEEE Trans Ind Inf, 19, 1260, 10.1109/TII.2022.3173996 · DOI
- Lyu, 2022, Privacy and robustness in federated learning: Attacks and defenses, IEEE Trans Neural Netw Learn Syst, 1
- Qu, 2020, Decentralized privacy using blockchain-enabled federated learning in fog computing, IEEE Internet Things J, 7, 5171, 10.1109/JIOT.2020.2977383 · DOI · Paper page
- Nakamoto, 2008, Bitcoin: A peer-to-peer electronic cash system, Decentralized Bus Rev, 21260
- Toyoda, 2020, Blockchain-enabled federated learning with mechanism design, IEEE Access, 8, 219744, 10.1109/ACCESS.2020.3043037 · DOI
- Zhang, 2020, Sablockfl: a blockchain-based smart agent system architecture and its application in federated learning, Int J Crowd Sci, 4, 133, 10.1108/IJCS-12-2019-0037 · DOI
- Lu, 2019, Blockchain and federated learning for privacy-preserved data sharing in industrial IoT, IEEE Trans Ind Inf, 16, 4177, 10.1109/TII.2019.2942190 · DOI · Paper page
- Blanchard, 2017, Machine learning with adversaries: Byzantine tolerant gradient descent, Adv Neural Inf Process Syst, 30
- Shayan, 2020, Biscotti: A blockchain system for private and secure federated learning, IEEE Trans Parallel Distrib Syst, 32, 1513, 10.1109/TPDS.2020.3044223 · DOI · Paper page
Cited by
Sorted by citations
Survey: federated learning data security and privacy-preserving in edge-Internet of Things
Li, H., Ge, L., & Tian, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10774-7 · Cited by 112
Federated Objective: Assessing Client Truthfulness in Federated Learning
Garofalo, M., Catalfamo, A., Colosi, M., & Villari, M. (2024) · 2024 IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825652 · Cited by 2
Metadata
- Authors
- Jin, S., Li, Y., Chen, X., Li, R., & Shen, Z.
- Affiliations
- Beijing Jiaotong University (CN)
- Corresponding author
- Li, Yong
- Venue
- Journal of Information Security and Applications
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 77
- Pages
- 103580
- ISSN
- 2214-2126 (ISSN-L)
- OpenAlex
- W4386277012
- Cited by
- 7
- References
- 7
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 1
- FWCI
- 0.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Information Security and Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- federated learning, blockchain, label-flipping attacks, fairness, digital signatures, public-key encryption, privacy preservation
About these checks
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 ›
Are you an author, or do you see a problem with this page? Request a review
