The DOI matches its Crossref record; 66 of the 67 references it cites were found in a registry or an index.
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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McMenamin, C., & Daza, V. (2024) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc59979.2024.10634414
Blockchain-based Federated Learning System for Intelligent Traffic Management
Du, J., Wang, X., Wu, Q., & Wang, C. (2024) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds62779.2024.10667585
“Weng et al.[15] proposed a framework named DeepChain, which provided a value-driven incentive mechanism based on blockchain to force the participants to behave correctly and ensure the data privacy for each participant.”
BackgroundPIM-FFL: Evaluating Participation Index Metrics for Fairness Enhancement in Federated Learning
Batool, H., Xu, J., Rehman, A. U., Kaur, G., & Bharany, S. (2024) · International Conference on Sustainable Expert Systems (ICSES) · doi:10.1109/icses63445.2024.10763231
“DeepChain [16], approach assesses client contributions based on data size and ensures fair reward distribution and penalty allocation.”
AI-Powered Early Detection of Diabetes Using Machine Learning on Electronic Health Records
Pukale, S. G., Jagdale, S., Bhandari, A., & Shinde, S. (2025) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset25122171
AI-Based Crypto Tokens: The Illusion of Decentralized AI?Preprint
Mafrur, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5241453
Mao, M., Zhu, X., Chen, J., Zhang, L., Liao, G., & Diao, Z. (2025) · IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) · doi:10.1109/hpsc66065.2025.00023
“For example, Weng et al. designed a secure and fair deep learning framework named DeepChain to integrate smart contracts to automate incentive mechanisms in blockchain-based federated learning [19].”
Blockchain marries privacy-enhancing computation
Weng, J., Weng, J., Wang, B.-M., & Dong, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34717-7.00021-0
Self-citationFair and Private Federated Learning via Game-Theoretic Incentives
Ma, R. (2025) · IEEE International Conference on Pattern Recognition, Machine Vision and Artificial Intelligence (PRMVAI) · doi:10.1109/prmvai65741.2025.11108639
Khivsara, B. A., & Rathore, M. (2025) · Journal of The Institution of Engineers (India) Series C · doi:10.1007/s40032-025-01244-2
Federated Learning with Blockchain-Enhanced Model Explainability
Kaur, G., Wasson, V., & Kaur, S. (2025) · Blockchain technologies · doi:10.1007/978-981-95-1394-9_5
Wang, Y., Mu, J., Yao, Z., Wang, J., Ouyang, W., Zhou, Q., Xu, F., & Chen, H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3635726
“Tensor decomposition techniques, on the other hand, provide another avenue for model compression in FL [31].”
Surapaneni, P., Chigurupati, S., & Bojjagani, S. (2025) · doi:10.1002/9781394311057.ch6
Decentralized Federated Learning for Distributed Energy Resources and Networks
Karim, M. H., Rao, S. P., & Ranganathan, P. (2025) · North American Power Symposium (NAPS) · doi:10.1109/naps66256.2025.11272348
A Distributed Peer-to-Peer NLP Model Training Framework with Blockchain Incentives
Penumada, A. R., Gundapaneni, D. J., Chennupati, A., & Belwal, M. (2025) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss67709.2025.11294535
Decentralising AI: Can Blockchain Technology Solve Data Privacy Challenges in Machine Learning?
Murphy, D. G., Lundie, A., Sun, R., & Wilkinson, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-7121-2_4
Xiao, D., Tang, J., & Chen, L. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11323165
“Weng et al. [23] introduced DeepChain, a blockchain-based decentralized FL framework that uses the threshold Paillier algorithm to protect gradient privacy.”
Token design strategies for entrepreneurial crypto projects, a systematic literature review
Mohammad, Z. A., & Bauer, J. (2026) · Frontiers in Blockchain · doi:10.3389/fbloc.2026.1741756
“Examples like DeepChain, which utilizes zero-knowledge proofs for privacy-preserving computations in AI and deep learning contexts, showcase the innovative applications of tokenomics in enhancing blockchain functionalities (Weng et al., 2019).”
Exploring the Advances of Artificial Intelligence in Cryptocurrencies and Darknet Forensics
El-Kady, R. M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/406039
Intégration de la Blockchain à l’Internet des Objets
Moudoud, H. (2022) · theses.fr (ABES) · doi:10.70675/1827dabfza012z4967z95fdz37a1359510f2
Privacy-Preserving Protocols for E-Commerce Payments
Srishti, P., Tripathi, N., Padgilwar, R. A., Patel, P. L., Ismatov, S., & Yusubov, I. (2026) · International Conference on Emerging Smart Computing and Informatics (ESCI) · doi:10.1109/esci68015.2026.11493383
Wang, A., Liu, J., Wen, J., Luo, Y., Fan, Z., Yang, L., Hu, X., Luo, R., Yu, Y., Li, S., et al. (2026) · Advanced Science · doi:10.1002/advs.202520562
Block chain assisted deep learning framework for intrusion detection in Zigbee based IoT network
Rahul, J., Malghan, R. L., Janani, K., & Rao, K. M. C. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01519-2
“Nevertheless, the latest improvements in the lightweight blockchain protocol and refined neural network have enabled the development of a small-scale efficient structure that can be implemented in the IoT setting [12].”
Mobility-Aware Sustainable Federated Learning via Auction Mechanisms in Vehicular Edge Computing
Liu, G., Li, X., Hao, J., Xu, G., Wang, X., & Leung, V. C. M. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3702763
“In the context of incentive mechanisms for FL, early work tended to maximize the utility of a single party [37], [38], [39].”
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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