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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Use of secure robust aggregation is currently recognized as an effective means for defending against Byzantine attacks.
“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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“Miao et al. [32] proposed a privacy-preserving byzantine-robust federated learning to mitigate the impact of central servers and malicious clients.”
BackgroundCai, L., Dai, Y., Hu, Q., Zhou, J., Zhang, Y., & Jiang, T. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3405070 · Cited by 24 · see citing works
Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges
Sandeepa, C., Zeydan, E., Samarasinghe, T., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3513832 · Cited by 26 · see citing works
“Authors in [210] introduce a Byzantine-robust, privacy-preserving FL system using blockchain, and the paper in [211] discusses a blockchain-based reputation-aware FL system for enhancing trust.”
Gupta, I., Saxena, D., Singh, A. K., & Lee, C. (2023) · IEEE Systems Journal · doi:10.1109/jsyst.2023.3272611 · Cited by 22 · see citing works
“A blockchain-based scheme named privacy-preserving byzantine-robust federated learning (PBFL) is proposed by Miao et al. [35] for privacy protection.”
MethodZhang, L., Fang, G., & Tan, Z. (2025) · Cluster Computing · doi:10.1007/s10586-024-04894-6 · Cited by 21 · see citing works
“Miao et al. [19] proposed a blockchain-based privacy-preserving Byzantine robust FL scheme to mitigate the influence of malicious clients.”
VPPFL: A verifiable privacy-preserving federated learning scheme against poisoning attacks
Huang, Y., Yang, G., Zhou, H., Dai, H., Yuan, D., & Yu, S. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103562 · Cited by 21 · see citing works
Building Trusted Federated Learning: Key Technologies and Challenges
Chen, D., Jiang, X., Zhong, H., & Cui, J. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12010013 · Cited by 20 · see citing works
“[61] uses blockchain to facilitate transparent processes and enforce regulations.”
Toward Byzantine-Robust Distributed Learning for Sentiment Classification on Social Media Platform
Zhang, H., Wu, J., Pan, Q., Bashir, A. K., & Omar, M. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3361465 · Cited by 20 · see citing works
“Privacy-preserving Byzantine-robust federated learning (PBFL) [26] leveraged blockchain to realize transparent gradient aggregation while using cosine similarity to filter out malicious gradients [26].”
Retracted: An Efficient Privacy-Preserving Machine Learning for Blockchain Network
Sonkar, A., Sahu, S. K., Nayak, A., Sahu, D., Verma, P., & Tiwari, R. (2024) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit61985.2024.10627061 · Cited by 20 · see citing works
RetractedBIT-FL: Blockchain-Enabled Incentivized and Secure Federated Learning Framework
Ying, C., Xia, F., Wei, D. S. L., Yu, X., Xu, Y., Zhang, W., Jiang, X., Jin, H., Luo, Y., Zhang, T., et al. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3477616 · Cited by 20 · see citing works
CareFL: C ontribution Guided Byz a ntine- R obust F e derated Learning
Dong, Q., Yang, S., Dai, Z., Gao, Y., Wang, S., Cao, Y., Fu, A., & Susilo, W. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3477912 · Cited by 19 · see citing works
“For any w , b w , we have [25] F ( b w) ≥ F (w) + ⟨ ▽ F (w), b w − w ⟩ + µ Assumption 2: The gradient of the empirical loss function ▽ L ( x , w , y ) at the optimal global model w is bounded.”
BackgroundMethodResultInfluentialAhakonye, L. A. C., Nwakanma, C. I., Lee, J. M., & Kim, D. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100354 · Cited by 19 · see citing works
Local differentially private federated learning with homomorphic encryption
Zhao, J., Huang, C., Wang, W., Xie, R., Dong, R., & Matwin, S. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05378-x · Cited by 21 · see citing works
“Privacy-preserving techniques, including HE, secure MPC, and DP, have been widely studied and applied to FL [30, 31].”
BackgroundResponsible Recommendation Services with Blockchain Empowered Asynchronous Federated Learning
Ali, W., Kumar, R., Zhou, X., & Shao, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3633520 · Cited by 19 · see citing works
“Weare not the first who realize the significance of blockchain for temper resilient FL. Research attempts have been made in diverse areas [12, 29, 34, 37].”
InfluentialPEFL: Privacy-Preserved and Efficient Federated Learning With Blockchain
Tian, L., Lin, F., Gan, J., Jia, R., Zheng, Z., & Li, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3479328 · Cited by 19 · see citing works
“By recording intermediate results and the global model during FL training on the blockchain, not only can potential malicious behavior by central servers be prevented but system transparency and overall trustworthiness can also be enhanced [6].”
A Privacy-Enhanced Traceable Anonymous Transaction Scheme for Blockchain
Xue, L., Huang, H., Xiao, F., Li, Q., & Wang, Z. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3526049 · Cited by 18 · see citing works
A Scheme of Robust Privacy-Preserving Multi-Party Computation via Public Verification
Gai, K., Wang, D., Yu, J., Zhu, L., & Meng, W. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3555284 · Cited by 18 · see citing works
“Implementations of MPC are adopted in a number of domains, such as electronic auctions [3], [4], [5], [6], e-voting [7], [8], [9], and machine learning [10], [11].”
VDFChain: Secure and verifiable decentralized federated learning via committee-based blockchain
Zhou, M. L., Yang, Z., Yu, H., & Yu, S. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103814 · Cited by 18 · see citing works
Li, Q., Yi, X., Nie, J., & Zhu, Y. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3375298 · Cited by 17 · see citing works
“…j = 1 ω ji = 1, sim ( h, i ) represents the similarity between task h and task i , This paper uses a modified cosine function to measure similarity [33], [34], which is In which, H and I represent the collection of vehicles executing tasks h and task i , respectively, Γ = H ∩ I represents a set of…”
SF-CABD: Secure Byzantine fault tolerance federated learning on Non-IID data
Lin, X., Li, Y., Xie, X., Ding, Y., Wu, X., & Ge, C. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111851 · Cited by 17 · see citing works
Blockchain-Based Trusted Federated Learning with Pre-Trained Models for COVID-19 Detection
Bian, G., Qu, W., & Shao, B. (2023) · Electronics · doi:10.3390/electronics12092068 · Cited by 16 · see citing works
Toward Secure and Verifiable Hybrid Federated Learning
Du, R., Li, X., He, D., & Choo, K. R. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3357288 · Cited by 16 · see citing works
“Another major challenge in FL is that it is vulnerable to Byzantine attacks [14], [15].”
BackgroundSelf-citationFlexibleFL: Mitigating poisoning attacks with contributions in cloud-edge federated learning systems
Zhao, Y., Cao, Y., Zhang, J., Huang, H., & Liu, Y. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120350 · Cited by 16 · see citing works
Anitha, R., & Murugan, M. (2024) · International Journal of Communication Systems · doi:10.1002/dac.5955 · Cited by 16 · see citing works
“Miao et al. [19] developed a Privacy-preserving Byzantine-robust federated learning (PBFL) approach for improved robustness and privacy in the blockchain system.”
Privacy-preserving federated learning compatible with robust aggregators
Alebouyeh, Z., & Bidgoly, A. J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110078 · Cited by 16 · see citing works
BPS-FL: Blockchain-Based Privacy-Preserving and Secure Federated Learning
Yu, J., Yao, H., Ouyang, K., Cao, X., & Zhang, L. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020053 · Cited by 15 · see citing works
MPV: Enabling Fine-Grained Query Authentication in Hybrid-Storage Blockchain
Liu, Q., Peng, Y., Xu, M., Jiang, H., Wu, J., Wang, T., Peng, T., & Wang, G. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3359173 · Cited by 15 · see citing works
“A BLOCKCHAIN is a distributed append-only ledger built upon a chain of blocks maintained by a peer-to-peer network [1].”
BackgroundTowards blockchain-enabled decentralized and secure federated learning
Ma, X., Xu, D., & Wolter, K. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120368 · Cited by 15 · see citing works
Byzantine-robust Federated Learning via Cosine Similarity Aggregation
Zhu, T., Guo, Z., Yao, C., Tan, J., Dou, S., Wang, W., & Han, Z. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110730 · Cited by 15 · see citing works
Zhang, C., Zhang, X., Yang, X., Liu, B., Zhang, Y., & Zhou, R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103131 · Cited by 16 · see citing works
Anonymous Federated Learning via Named-Data Networking
Agiollo, A., Bardhi, E., Conti, M., Fabbro, N. D., & Lazzeretti, R. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.11.009 · Cited by 15 · see citing works
Basudan, S. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.05.005 · Cited by 14 · see citing works
A Two-Stage Approach for Fair Data Trading Based on Blockchain
Chen, F., Zhang, H., Xiang, T., & Liu, J. K. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3482716 · Cited by 14 · see citing works
“The general idea is to leverage the blockchain smart contract as a kind of automatic TTP. Compared with traditional TTP, blockchain smart contract empowers distributed, decentralized trust [12], [13], [14].”
BackgroundA Practical Federated Learning Framework With Truthful Incentive in UAV-Assisted Crowdsensing
Xie, L., Su, Z., Wang, Y., & Li, Z. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3484946 · Cited by 14 · see citing works
Blockchain-Enabled Vehicle Lifecycle Management With Predictive Maintenance Using Federated Learning
Peelam, M. S., Shah, K., Chamola, V., & Sikdar, B. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3487874 · Cited by 14 · see citing works
“FL and blockchain integration introduce new adversarial threats [53], [54].”
Blockchain Security and Privacy: Threats, Challenges, Applications, and Tools
Alghuried, A., Alkinoon, M., Mohaisen, M., Wang, A., Zou, C. C., & Mohaisen, A. (2025) · Distributed Ledger Technologies Research and Practice · doi:10.1145/3716323 · Cited by 14 · see citing works
Auditable Federated Learning With Byzantine Robustness
Liang, Y., Li, Y., & Shin, B. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3266019 · Cited by 13 · see citing works
“The recent FL works [14], [15], [16], which attempt to defend against poisoning attacks while preserving privacy, use two curious-but-honest servers and assume that the servers do not collude with one another or clients.”
InfluentialFedSIGN: A sign-based federated learning framework with privacy and robustness guarantees
Guo, Z., Xu, L., & Zhu, L. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103474 · Cited by 13 · see citing works
Dynamic behavior assessment protocol for secure Decentralized Federated Learning
Khan, S., Gomes, J., Rehman, M. H. U., & Svetinović, D. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100956 · Cited by 13 · see citing works
A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis
Tang, Y., Zhang, Y., Niu, T., Li, Z., Zhang, Z., Chen, H., & Zhang, L. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.030084 · Cited by 20 · see citing works
A Generic Blockchain-based Steganography Framework with High Capacity via Reversible GAN
Chen, Z., Zhu, L., Jiang, P., He, J., & Zhang, Z. (2024) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom52122.2024.10621377 · Cited by 13 · see citing works
“In the blockchain-based steganography [8], [9], the sender and the receiver establish a covert channel through the blockchain network [10], [11].”
MethodFu, X., Xiong, L., Li, F., Yang, X., & Xiong, N. N. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3439437 · Cited by 11 · see citing works
“More recently, Miao et al. [32] introduced a privacy-preserving mechanism founded on blockchain technology.”
Chen, C., Hao, Y., Kumari, S., & Amoon, M. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3476969 · Cited by 12 · see citing works
“Miao et al. [26] devised a privacy-preserving protocol augmented by blockchain technology to mitigate threats from malicious clients.”
Bc²FL: Double-Layer Blockchain-Driven Federated Learning Framework for Agricultural IoT
Ding, Q., Yue, X., Zhang, Q., Xiong, Z., Chang, J., & Zheng, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3485208 · Cited by 12 · see citing works
“This heterogeneity inherently poses challenges for each farm in accessing genuinely valuable knowledge from others [19].”
Guan, M., Bao, H., Li, Z., Pan, H., Huang, C., & Dai, H. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103304 · Cited by 11 · see citing works
The Convergence of AI/ML and Cybersecurity: Advancing Digital Forensic Techniques
Iyengar, S. S., Nabavirazavi, S., Hariprasad, Y., Prasad, H. B., & Mohan, C. K. (2025) · Signals and communication technology · doi:10.1007/978-3-031-89327-8_4 · Cited by 12 · see citing works
Multi-Region Asynchronous Swarm Learning for Data Sharing in Large-Scale Internet of Vehicles
Yin, H., Huang, X., Wu, Y., Liang, C., & Chen, Q. (2023) · IEEE Communications Letters · doi:10.1109/lcomm.2023.3314662 · Cited by 11 · see citing works
“To record the model to the blockchain, firstly, the RSU will validate the received site and evaluate the model accuracy using a small dataset D 0 , which could determine the more “promising” direction of the global model update [12].”
Sen, A., Heng, S., & Tan, S. C. (2025) · IEEE Access · doi:10.1109/access.2025.3593953 · Cited by 12 · see citing works
“For instance, Guan et al. [195] and Miao et al. [196] applied HE in blockchain-based FL to guarantee poisoning attacks avoidance as well as data theft.”
Farrukh, H., Zafar, S., Rehman, Z. U., Shah, A. A., & Alshammry, N. (2025) · Electronics · doi:10.3390/electronics14244952 · Cited by 15 · see citing works
Sharma, D. D. (2023) · IET Blockchain · doi:10.1049/blc2.12041 · Cited by 10 · see citing works
Edge server enhanced secure and privacy preserving federated learning
Xu, Y., Mao, Y., Li, J., Chen, X., & Wu, S. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110465 · Cited by 11 · see citing works
Privacy-Preserving Vertical Federated Learning With Tensor Decomposition for Data Missing Features
Liao, T., Fu, L., Zhang, L., Yang, L., Chen, C., Ng, M. K., Huang, H., & Zheng, Z. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3552033 · Cited by 10 · see citing works
“Several solutions have been proposed to ensure privacy in data transmission, such as federated learning methods based on homomorphic encryption (HE) [17] and secure multi-party computation (SMC) [18].”
FedG2L: a privacy-preserving federated learning scheme base on “G2L” against poisoning attack
Xu, M., & Li, X. (2023) · Connection Science · doi:10.1080/09540091.2023.2197173 · Cited by 9 · see citing works
“…Shejwalkar & Houmansadr, 2021; Sun et al., 2021) PEFL (Liu et al., 2021) ✗ ✗ ShiledFL (Ma et al., 2022) (Hou et al., 2021; Li et al., 2022; Yang et al., 2023) PBFL (Miao et al., 2022) ✗ ✗ Our scheme: FedG2L Note: PGP: Protect gradient privacy; WBG: Without benign gradient; PoP: Period of poisoning.”
BackgroundEfficient and Secure Aggregation Framework for Federated-Learning-Based Spectrum Sharing
Li, W.-W., Chen, G.-R., Zhang, X., Wang, N., Ouyang, D., & Chen, C.-X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3357575 · Cited by 9 · see citing works
“Furthermore, this security strategy cannot prevent collusion between the central server and participants, even when using homomorphic encryption techniques [11] and blockchain [20].”
BackgroundMethodLiu, L., Li, J., & Wang, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124699 · Cited by 9 · see citing works
- 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 › Artificial Intelligence › 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
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.
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