The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems
Open accessAbstract
Federated learning enables clients to train a machine learning model jointly without sharing their local data. However, due to the centrality of federated learning framework and the untrustworthiness of clients, traditional federated learning solutions are vulnerable to poisoning attacks from malicious clients and servers. In this paper, we aim to mitigate the impact of the central server and malicious clients by designing a Privacy-preserving Byzantine-robust Federated Learning (PBFL) scheme based on blockchain. Specifically, we use cosine similarity to judge the malicious gradients uploaded by malicious clients. Then, we adopt fully homomorphic encryption to provide secure aggregation. Finally, we use blockchain system to facilitate transparent processes and implementation of regulations. Our formal analysis proves that our scheme achieves convergence and provides privacy protection. Our extensive experiments on different datasets demonstrate that our scheme is robust and efficient. Even if the root dataset is small, our scheme can achieve the same efficiency as FedSGD.
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“The use of secure robust aggregation is currently recognized as an effective means for defending against Byzantine attacks (Li et al. 2023d;Miao et al. 2022;Wan et al. 2022).”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Miao et al. 2022
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Cites
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
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Protocols for secure computations
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Large Scale Distributed Deep Networks
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Homomorphic Encryption for Arithmetic of Approximate Numbers
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Research works printed but not linked: 1
- 10.1109/MNET.011.2000263 · DOI · Paper page
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Items 301–301 of 301
FedLCD: A Federated Learning Framework with Layer-Wise Consistency Detection
Cui, X., & Wang, H. (2026) · Applied Sciences · doi:10.3390/app162010119
Metadata
- Authors
- Miao, Y., Liu, Z., Li, H., Choo, K.-K. R., & Deng, R. H.
- Affiliations
- Xidian University (CN)
- University of Electronic Science and Technology of China (CN)
- The University of Texas at San Antonio (US)
- Singapore Management University (SG)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2022
- Volume
- 17
- Pages
- 2848-2861
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W4289656845
- Semantic Scholar
- 0070997f3305097cce932a073a09e2bed9c8d406
- Cited by
- 303
- References
- 41
- Citations by year
- 2026: 57 · 2025: 101 · 2024: 97 · 2023: 44 · 2022: 4
- FWCI
- 20.23
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- Funders
- National Natural Science Foundation of China (62072361, 62125205)
- Key Research and Development Program of Shaanxi (2022GY-019)
- Fundamental Research Funds for the Central Universities (JB211505)
- Henan Key Laboratory of Network Cryptography Technology and the State Key Laboratory of Mathematical Engineering and Advanced Computing (LNCT2020-A06)
- Cloud Technology Endowed Professorship
- 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, privacy preservation, Byzantine robustness, blockchain, poisoning attacks, malicious clients, malicious server, cosine similarity, fully homomorphic encryption, secure aggregation, convergence analysis
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