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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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From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [21]
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“The developed prototype proves DeepChain’s performance in terms of confidentiality, auditability, and fairness.”
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张, 沁., 朱, 建., Gao, S., 熊, 泽., 丁, 庆., & 朴, 桂. (2022) · Scientia Sinica Informationis · doi:10.1360/ssi-2022-0020 · Cited by 13
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“Moreover, the clients’ training costs or even their arrival pattern are unknown to the FL system (e.g., [24]), as clients in general have different data sizes or distributions to contribute as well as different model training time per iteration.”
BackgroundBlockchain-based decentralized federated learning: A secure and privacy-preserving system
Zhao, S., Wu, Y., Sun, R., Qian, X., Zi, D., Xie, Z., Tong, E., Niu, W., Liu, J., & Han, Z. (2021) · 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-dependsys53884.2021.00150 · Cited by 12
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${\sf FedRLChain}$: Secure Federated Deep Reinforcement Learning With Blockchain
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“Weng et al. [19] proposed DeepChain, a blockchain-enabled distributed deep learning framework with a special focus on privacy, auditability, and incentivization to train DL models.”
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Ahmed, A., & Choi, B. J. (2023) · Electronics · doi:10.3390/electronics12153259 · Cited by 12
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“…communication between users and cloud providers (Avizheh et al., 2019), frameworks like VeriBlock for verifiable computing using Trusted Execution Environments (TEE) (Maddali et al., 2020), and decentralized training of NNs using blockchain (Weng et al., 2019; Wang et al., 2023; Liu et al., 2020).”
Traceability and Identity Protection in Smart Agricultural IoT System Framework Based on Blockchains
Xie, M., Liu, J., Chen, S., Lin, M., Xu, G., Xu, Z., & Chang, Z. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3565593 · Cited by 12
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Blockchain-based Framework for Scalable and Incentivized Federated Learning
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Sustainable Federated Learning with Long-term Online VCG Auction Mechanism
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Long-Term Client Selection for Federated Learning With Non-IID Data: A Truthful Auction Approach
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Machine Learning And Blockchain: A Promising Future
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MethodEnhancement of UAV Data Security and Privacy Via Ethereum Blockchain Technology
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Zhang, W., Du, M., Guo, X., & Xiong, N. N. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3509264 · Cited by 12
“Deepchain [43] proposed a value-driven incentive mechanism to encourage participants to collaborate actively.”
DA-FL: Blockchain Empowered Secure and Private Federated Learning With Anonymous Authentication
Xiong, H., Zhao, Y., Xia, Y., Zhang, M., & Yeh, K. (2025) · IEEE Transactions on Reliability · doi:10.1109/tr.2025.3587088 · Cited by 13
“On the other hand, insufficient identity authentication cannot effectively defend against poisoning attacks [14], [15].”
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Lightweight Privacy-Preserving Federated Incremental Decision Trees
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BackgroundThe Role of Artificial Intelligence and Robotic Solution Technologies in Metaverse Design
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Mafrur, R. (2025) · IET Blockchain · doi:10.1049/blc2.70015 · Cited by 13
“Another example is DeepChain [91], a framework that combines federated deep learning with blockchain-driven rewards.”
Toward Blockchain for Intelligent Systems
Tsai, C. (2021) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2021.3076611 · Cited by 8
“A few studies 5 ; 10 ; 12 not only presented different applications but also pointed out that distributed computing is a very useful way to integrate deep learning (e.g., federated learning, http://federated-ml.org/) and BC.”
BackgroundMethodFederated Learning with Anomaly Client Detection and Decentralized Parameter Aggregation
Liu, S., & Shang, Y. (2022) · International Conference on Dependable Systems and Networks workshops · doi:10.1109/dsn-w54100.2022.00016 · Cited by 8
Li, D., Guo, Q., Yang, C., & Yan, H. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/5338876 · Cited by 8
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain ApproachPreprint
Cao, M., Zhang, L., & Cao, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13092 · Cited by 8
“Combining blockchain and FL, the authors in [35] propose a DeepChain architecture.”
A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber SecurityPreprint
Ravi, V., Soman, K. P., Alazab, M., Sriram, S., & Simran, K. (2020) · doi:10.36227/techrxiv.11473377 · Cited by 8
Palacios, S., Ault, A. C., Krogmeier, J. V., Bhargava, B., & Brinton, C. G. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3192852 · Cited by 7
“DeepChain relies on the decentralized nature of blockchain technologies to provide a reward-based mechanism that can force participants to behave honestly [54].”
BackgroundMethodInfluentialA federated learning scheme for hierarchical protection and multiple aggregation
Wang, Z., Yu, X., Wang, H., & Xue, P. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109240 · Cited by 7
Lu, J., Huang, T., Cao, S., Yu, S., Jia, R., & Li, M. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3602314 · Cited by 8
“Current researchers have designed various incentive mechanisms for di ff erent FL systems to attract su ffi cient data owners to participate [30], [31], [32].”
Self-citationEdge computing and federated learning for privacy-preserving IoT analytics
Alatawi, M. N. (2025) · Journal on Wireless Communications and Networking · doi:10.1186/s13638-025-02545-x · Cited by 7
“Additionally, blockchain-based incentive frameworks [36] offer avenues to enhance node reliability, which we plan to integrate in future iterations.”
Secure Federated Learning for IoT using DRL-based Trust Mechanism
Al-Maslamani, N. M., Abdallah, M. M., & Çiftler, B. S. (2022) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc55113.2022.9824672 · Cited by 7
PrivMaskFL: A private masking approach for heterogeneous federated learning in IoT
Xiong, J., & Zhu, H. P. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.11.022 · Cited by 8
Jiang, Y., & Zhang, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3449224 · Cited by 7
“By providing a secure framework, blockchain facilitates the development of decentralized applications in both financial and engineering fields [2], [3], [4], [5], [6], [7], [8], [9], [10].”
BackgroundSelf-citationLiang, R., Jiang, Y., Zhu, F., Cheng, L., & Liu, H. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3706112 · Cited by 7
BPFL: Blockchain-Enabled Distributed Edge Cluster for Personalized Federated Learning
Firdaus, M., Noh, S., Qian, Z., & Rhee, K.-H. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1252-0_57 · Cited by 7
A Blockchain-Based Auditable Semi-Asynchronous Federated Learning for Heterogeneous Clients
Zhuohao, Q., Firdaus, M., Noh, S., & Rhee, K.-H. (2023) · IEEE Access · doi:10.1109/access.2023.3335603 · Cited by 7
Wang, G., Li, Y., Liu, R., Tong, F., Pan, J., Zuo, F., & He, X. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3344467 · Cited by 7
“The blockchain technology is characterized by decentralization, anonymity, anti-tampering, and traceability, which provides a new way to study privacy preservation and information security in the IoT [35], [36], [37].”
BackgroundTowards trustless prediction-as-a-service
Santhosh, G., Vita, F. D., Bruneo, D., Longo, F., & Puliafito, A. (2019) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp.2019.00068 · Cited by 7
“For example, [7] presents a blockchain-based system for the collaborative privacy-preserving training of neural networks guaranteeing computation auditability and data confidentiality.”
BackgroundAI and Computer Vision Technologies for Metaverse
The, T. H., Pham, Q., Pham, X., Do‐Duy, T., & Gadekallu, T. R. (2023) · Wiley eBooks · doi:10.1002/9781394160013.ch5 · Cited by 7
Jahns, E., Stojkov, M., & Kinsy, M. A. (2025) · IEEE Access · doi:10.1109/access.2025.3561721 · Cited by 8
“Weng et al. [81] propose DeepChain, a Federated Learning framework that uses the BlockChain to provide auditability and fairness during training.”
BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems
Hou, D., & Zhang, J. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386918 · Cited by 6
“Weng et al. [19] developed a DeepChain that has an incentive-driven mechanism based on participants’ contribution from correct behaviors in blockchain.”
BackgroundSelf-citationFair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on Blockchain
Du, B., Wang, H., Yuxuan, L., Zhao, J., Ma, Y., & Huang, R. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3673656 · Cited by 6
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
Liu, Y., Guo, B., Li, N., Ding, Y., Zhang, Z., & Yu, Z. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3423319 · Cited by 13
“DeepChain [147] introduces a collaborative training method with incentives for training DNNs.”
Protecting machine learning systems using blockchain: solutions, challenges and future prospects
Hajlaoui, R., Dhahri, S., Mahfoudhi, S., Moulahi, T., & Alotibi, G. N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19993-0 · Cited by 7
A Blockchain-Based Federated Learning
Barai, A. K., Bhadoria, R. S., Bagwari, J., & Андреевич, П. И. (2020) · Advances in data mining and database management book series · doi:10.4018/978-1-7998-5876-8.ch008 · Cited by 6
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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