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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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Self-citationBlockchain‐Based Federated Learning Methodologies in Smart Environments for Drone Technology
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A review on building blocks of decentralized artificial intelligence
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An, J., Tang, S., Sun, X., Gui, X., He, X., & Wang, F. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3486185 · Cited by 16
An Intelligent Distributed Ledger Construction Algorithm for IoT
Rawlins, C. C., & Jagannathan, S. (2022) · IEEE Access · doi:10.1109/access.2022.3146343 · Cited by 16
Li, S., Li, F., Wang, K., Qi, G., & Li, H. (2022) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2022.102568 · Cited by 16
Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework
AlAsqah, M., Moulahi, T., Zidi, S., & Alabdulatif, A. (2023) · Applied Sciences · doi:10.3390/app13021035 · Cited by 19
Laidi, R., Merabtine, N., Djenouri, D., Latif, S., Qadir, H. A., Djenouri, Y., & Balasingham, I. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3557475 · Cited by 16
“Weng et al. [51] discussed how such attacks can harm blockchain-based FL systems and propose mechanisms to detect and prevent them.”
Blockchain-Based Distributed Key Management Approach Tailored for Smart Grid
Baza, M., Fouda, M. M., Nabil, M., Eldien, A. S. T., Mansour, H. A. E.-K., & Mahmoud, M. M. E. A. (2020) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-030-35642-2_11 · Cited by 16
Lu, J., Pan, B., Seid, A. M., Li, B., Hu, G., & Wan, S. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3227270 · Cited by 17
“In the literature, many efforts have been devoted to designing Truthful incEntive mechAnisms ( TEA ) for HFL to solve information disclosure and client heterogeneity problem [13], [14], [15], [16].”
BackgroundDInEMMo: Decentralized Incentivization for Enterprise Marketplace Models
Marathe, A., Narayanan, K., Gupta, A., & Manoj, P. R. (2018) · IEEE International Conference on High Performance Computing Workshops (HiPCW) · doi:10.1109/hipcw.2018.8634320 · Cited by 16
Blockchain-Based Federated Learning Against End-Point Adversarial Data Corruption
Sun, Y., Esaki, H., & Ochiai, H. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00119 · Cited by 14
Blockchain-based Trustworthy Federated Learning ArchitecturePreprint
Lo, S. K., Liu, Y., Lu, Q., Wang, C., Xu, X., Paik, H.-Y., & Zhu, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.06912 · Cited by 16
“Blockchain has been utilised for IoT and federated learning to maintain data integrity with its immutability [9]–[11].”
MethodBlockFedML: Blockchained Federated Machine Learning Systems
Wang, S. (2019) · International Conference on Intelligent Computing, Automation and Systems (ICICAS) · doi:10.1109/icicas48597.2019.00162 · Cited by 15
“[14] proposed the "DeepChain" framework to solve this problem.”
BackgroundExecution of a Federated Learning process within a smart contract
Short, A. R., Leligou, H. C., & Theocharis, E. (2021) · International Conference on Consumer Electronics · doi:10.1109/icce50685.2021.9427734 · Cited by 16
“More specifically, relevant research has shown that such solutions can offer improved auditability [1][2], accountability [3], are able to store model updates in a decentralized manner [4], improve security [5] and can be used to facilitate incentive mechanisms [6].”
BackgroundDAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data
Lee, J., & Kim, W. (2022) · Sensors · doi:10.3390/s22218263 · Cited by 15
“Otherwise, reliable nodes can be recruited for the consensus procedure based on their reputations [63] or incentive systems [28].”
BackgroundMa, Z., Yuan, X., Liang, K. F., Feng, J., Zhu, L., Zhang, D., & Yu, F. R. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.11.029 · Cited by 15
Personalized federated learning for heterogeneous data: A distributed edge clustering approach
Firdaus, M., Noh, S., Qian, Z., Larasati, H. T., & Rhee, K.-H. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023475 · Cited by 16
“Then, the number of K clients ( C k ) need to register to the BPFL system by sending asset statement (refers to [36]) validation with their pseudo-public key address Pub psC through Eq (4.1).”
MethodInfluentialDeep Learning for Cyber Security Applications: A Comprehensive SurveyPreprint
Vinayakumar, R., Alazab, M., Soman, K. P., Srinivasan, S., Venkatraman, S., Pham, Q., & Simran, K. (2021) · doi:10.36227/techrxiv.16748161.v1 · Cited by 19
CUNA: A privacy preserving medical records storage in cloud environment using deep encryption
Gayathri, S., & Gowri, S. (2022) · Measurement Sensors · doi:10.1016/j.measen.2022.100528 · Cited by 14
A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks
Firdaus, M., Larasati, H. T., & Rhee, K.-H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.039487 · Cited by 16
Liu, Y., Yin, J., Zhang, W., An, C., Xia, Y., & Zhang, H. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3523350 · Cited by 17
“DeepChain [114] protects the participants’ privacy and enables the audibility of model updates.”
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
Decentralized Federated Learning for Private Smart Healthcare: A Survey
Cheng, H., Qu, Y., Liu, W., Gao, L., & Zhu, T. (2025) · Mathematics · doi:10.3390/math13081296 · Cited by 14
Privacy preserving human activity recognition framework using an optimized prediction algorithm
Kumar, K. V., & Harikiran, J. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v11.i1.pp254-264 · Cited by 14
Sharma, P. K., Gope, P., & Puthal, D. (2022) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw55150.2022.00008 · Cited by 16
“Many researchers are embracing the unaltered features of blockchain, federated learning, and differential privacy to secure the IoT network [15] [16] [17].”
BackgroundReliable Collaborative Learning with Commensurate Incentive Schemes
Rahmadika, S., & Rhee, K.-H. (2020) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain50366.2020.00072 · Cited by 14
“It consists of the data size, data format, fields, and data description [19].”
MethodSecuring Federated Learning With Blockchain in the Medical Field: Systematic Literature Review
Wang, X., Xie, Y., Chen, X., Yang, J., Li, R., Gao, W., Yan, Z., Zhou, H., & Ye, Z. (2026) · Journal of Medical Internet Research · doi:10.2196/79052 · Cited by 21
“These tasks are executed on a single node, fostering a fully decentralized collaborative model [36,74,79,92].”
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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