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DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
Abstract
Deep learning can achieve higher accuracy than traditional machine learning algorithms in a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn tremendous attention from information security community, in which neither training data nor the training model is expected to be exposed. Federated learning is a popular learning mechanism, where multiple parties upload local gradients to a server and the server updates model parameters with the collected gradients. However, there are many security problems neglected in federated learning, for example, the participants may behave incorrectly in gradient collecting or parameter updating, and the server may be malicious as well. In this article, we present a distributed, secure, and fair deep learning framework named DeepChain to solve these problems. DeepChain provides a value-driven incentive mechanism based on Blockchain to force the participants to behave correctly. Meanwhile, DeepChain guarantees data privacy for each participant and provides auditability for the whole training process. We implement a prototype of DeepChain and conduct experiments on a real dataset for different settings, and the results show that our DeepChain is promising.
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“References [18, 21] used Shamir secret sharing and threshold Paillier encryption to protect local gradients, respectively, but both consume large computation overhead.”
From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [21]
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“Weng et al. [20] provide a distributed deep learning architecture called DeepChain that works with a value-driven incentive system to compel edge nodes to obediently take part in the training.”
BackgroundMChain-SFFL: Multi-Chain Aggregation Privacy Preserving for Server-Free Federated Learning
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Huang, Z., Yang, A., Weng, J., Chen, M.-R., Xiao, F., Liu, Y., & Yao, Y. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622406 · Cited by 4
“DeepChain [8] employs homomorphic encryption, smart contracts, and trusted execution environment (TEE) to achieve privacy-preserving and publicly auditable gradient aggregation, while it relies on TEE and specific blockchain.”
MethodSelf-citationDataHarbour: Enabling Decentalized AI Data Marketplace using Blockchain
Dave, M., Saraf, A. N. G. H. S., Kumar, R., & Bhattacharya, R. (2024) · IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS) · doi:10.1109/icbds61829.2024.10837338 · Cited by 4
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“Our proposed platform is compared with the features of these projects and summarized in Table I. DeepChain [29] and BlockFL [32] are dedicated to safeguarding privacy in machine learning.”
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Self-citationRefacing Defaced MRI with PixelCNN
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SRC-FD: Secure and Reliable Cryptocurrency Transaction Fraud Detection Under Hybrid Blockchain
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“…studies explore blockchains’ potential to secure FL frameworks through three architectural paradigms: Public Blockchain Solutions: To build a reliable and de-centralized federated fraud detection model, some studies deployed public blockchain to record uploaded local models [16], [17], [18], [19].”
LPV-FL: A Lightweight Privacy-Preserving and Verifiable Federated Learning Scheme
Lei, X., Wang, S., & Fan, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3584084 · Cited by 2
“1) Tightly Coupled BCFL [32]: FL clients are also blockchain nodes.”
Publicly Auditable Federated Learning With Privacy and Byzantine Robustness
Zeng, H., Yang, A., Weng, J., Chen, M., Xiao, F., Liu, Z., & Liu, Y. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3609353 · Cited by 2
“In recent years, many efforts have been made to design au-ditable FL, but most of them struggle to simultaneously attain public auditability, privacy, and Byzantine robustness of FL. DeepChain [11] and AP2FL [12] are highly insightful works.”
Self-citationPoFEL: Energy-Efficient Consensus for Blockchain-Based Hierarchical Federated Learning
Li, S., Hu, Q., Wang, Z., Xu, M., & Cai, Z. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3675091 · Cited by 3
“1) Task Publication: A user sends a learning task request to the BCFL network so that all BCFL nodes can receive the information and apply an evaluation scheme to determine whether they accept the task.”
Sun, Z., Wan, J., Wang, B., Cao, Z., Li, R., & He, Y. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.739259 · Cited by 2
“[9] provided an audit mechanism based on ∑-protocol [25] which generates proofs of correctness for each gradient.”
BackgroundPrivacy-preserving Aggregation Scheme for Blockchained Federated Learning in IoT
Fan, M., Yu, H., & Sun, G. (2021) · International Conference on UK-China Emerging Technologies (UCET) · doi:10.1109/ucet54125.2021.9674950 · Cited by 2
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Rakkini, M. J. J., & Geetha, K. (2023) · International Transactions in Operational Research · doi:10.1111/itor.13359 · Cited by 2
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“[11]advocated the use of DeepChain, a distributed, encrypted, and fair deep learning system, to address these problems.”
MethodPrivacy-Preserving and Reliable Distributed Federated Learning
Dong, Y., Zhang, L., & Xu, L. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0834-5_9 · Cited by 2
Wu, Y., Cheng, H., Guan, L., Liu, P., Chen, F., & Wang, M. (2023) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys60770.2023.00096 · Cited by 2
“To facilitate more active participation, incentive mechanisms [14] [15] [16] have been proposed in FL, which reward clients based on the accuracy improvements of their local models.”
Zhao, H., Guo, Y., Wang, M., Fan, F., Zhang, H., & Ma, Y. (2023) · International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI) · doi:10.1145/3653081.3653105 · Cited by 2
A Blockchain-Based Trustworthy Federated Learning Privacy Protection Scheme
Liu, X., Li, X., Gu, A., Lv, S., & Su, J. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570990 · Cited by 2
“Toprevent inference attacks, DeepChain [7] uses the classical threshold Paillier algorithm to secure data.”
MethodIncentive Mechanism of Blockchain-Based Reverse Auction for Federated Learning
Cui, B., & Dang, L. (2024) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd61410.2024.10580195 · Cited by 2
“There has been a lot of research to date using blockchain technology to address the lack of effective incentive mechanism in FL. Weng et al. [14]proposed a blockchain-based incentive mechanism.”
BackgroundBlockchain-based Federated Learning Data Sharing Model
Xie, K., Feng, Y., Xu, H., & Han, Z. (2024) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds62779.2024.10667505 · Cited by 2
Marzougui, F., ELLEUCH, M., & Kherallah, M. (2024) · Journal of Information assurance and security · doi:10.2478/ias-2024-0008 · Cited by 2
Mishra, S., & Chaurasiya, V. K. (2025) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70116 · Cited by 2
Doctrina: Blockchain 5.0 for Artificial Intelligence
Tulkinbekov, K., & Kim, D. (2025) · Applied Sciences · doi:10.3390/app15105602 · Cited by 2
Integrating blockchain with federated learning for distributed cloud data security
Rengarajan, A. (2024) · Journal of Informatics Education and Research · doi:10.52783/jier.v4i2.2742 · Cited by 2
Improving Security and Fairness in Federated Learning Systems
Short, A. R., Orfanoudakis, T., & Leligou, H. C. (2021) · International Journal of Network Security & Its Applications · doi:10.5121/ijnsa.2021.13604 · Cited by 3
“More recently, similar research was performed by J. Weng et al [19] who implemented a blockchain assisted Federated Learning setup that focuses on incentive mechanisms and auditability.”
BackgroundMethodFederated Learning at Mobile Edge Networks: A Tutorial
Lim, W. Y. B., Ng, J. S., Xiong, Z., Niyato, D. T., & Miao, C. (2022) · Wireless networks · doi:10.1007/978-3-031-07838-5_1 · Cited by 4
A privacy-preserving and correctness audit method in multi-party data sharing
Wan, J., Xun, H., Zhang, X., Feng, J., & Sun, Z. (2020) · International Conference on Cyberspace Innovation of Advanced Technologies (CIAT) · doi:10.1145/3444370.3444606 · Cited by 2
“Weng et al. [8] design a model named DeepChain to guarantee the confidentiality and auditability of messages using a non-interactive protocol to provide proof.”
MethodDecFL: An Ubiquitous Decentralized Model Training Protocol and Framework Empowered by Blockchain
Morsbach, F., & Salman Zubair, T. O. O. R. (2021) · ACM International Symposium on Blockchain and Secure Critical Infrastructure (BSCI) · doi:10.1145/3457337.3457842 · Cited by 2
“DeepChain [44], FLchain [29] and BlockFL [21] are prominent efforts to address the challenges related to federated model training using blockchain technologies.”
BackgroundPrivacy Shield: A System for Edge Computing using Asynchronous Federated Learning
Khalid, A., Aziz, Z., & Fathi, M. S. (2022) · Scientific Programming · doi:10.1155/2022/7465640 · Cited by 2
Zhang, P., Liu, G., Chen, Z., Guo, J., & Liu, P. (2022) · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · doi:10.1109/cvidliccea56201.2022.9824166 · Cited by 2
Local Model Quality Control Method Based on Credit Mortgage for Enterprise Credit Evaluation
Li, X., Chen, F., Jin, Y., Chen, Q., & Li, C. (2022) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw56584.2022.00013 · Cited by 2
“[5]–[7] to encourage clients to participate in training deep learning models, a tokenbased incentive mechanism was designed to distribute rewards to local devices based on contributions.”
BackgroundBASS: Blockchain-Based Asynchronous SignSGD for Robust Collaborative Data Mining
Xu, C., Qu, Y., Xiang, Y., Gao, L., Smith, D., & Yu, S. (2022) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa54385.2022.10032341 · Cited by 2
“Specifically, the methods of utilizing the blockchain include: (1) Encourage nodes to engage in the training and establish a positive reputation to earn trust [4], [13], [14]; (2) Cross-validate models, signatures, and global model aggregation to ensure proper training [4], [6], [15]; (3) Decentralize the global model…”
MethodFast privacy-preserving utility mining algorithm based on utility-list dictionary
Yin, C., & Li, Y. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04791-2 · Cited by 2
“The blockchain [17] can monitor abnormal behaviors of devices or servers.”
BackgroundLi, Y., Ling, H., Ren, X., Yu, C. Y., & Liu, T. (2023) · IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · doi:10.1109/ecice59523.2023.10383173 · Cited by 2
“Weng proposed a blockchain-backed deep learning framework with a value-driven incentive mechanism to preserve data privacy and ensure auditability during the entire training process [5].”
BackgroundVenkatesan, B., Mannanuddin, K., Chidambaranathan, S., Balajee, J. M., Rayapati, B. R., & Baskar, K. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-3597-0.ch009 · Cited by 2
Metadata
- Authors
- Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W.
- Affiliations
- Jinan University (CN)
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2019
- Volume
- 2018
- Pages
- 1-1
- ISSN
- 1545-5971 (ISSN-L)
- 1941-0018
- 2160-9209
- OpenAlex
- W2889157463
- Cited by
- 610
- References
- 67
- Citations by year
- 2026: 22 · 2025: 60 · 2024: 102 · 2023: 126 · 2022: 112 · 2021: 102 · 2020: 55 · 2019: 27
- FWCI
- 33.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Dependable and Secure Computing (publishedVersion)
- IACR Cryptology ePrint Archive (submittedVersion)
- Funders
- National Natural Science Foundation of China (U1736203, 61732021, 61472165, 61373158)
- Guangdong Provincial Special Funds for Applied Technology Research and Development and Transformation of Important Scientific and Technological Achieve (2016B010124009)
- National Key RD Program of China (Grant No. 2018YFB1402600)
- National Key RD Plan of China (2017YFB0802203, 2018YFB1003701)
- Guangdong Provincial Engineering Technology Research Center on Network Security Detection and Defence (2014B090904067)
- 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
- privacy-preserving deep learning, federated learning, blockchain, incentive mechanisms, auditability, data privacy, malicious server, participant behavior, distributed deep learning
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