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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“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]
Citations
Cites
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1 of the 67 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
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Items 601–610 of 610
Prity, J. A., & Mahdi, A. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11545667
“Current blockchain-FL systems suffer from: (1) on-chain congestion from storing full models [11], (2) static incentives vulnerable to Sybil attacks [18], and (3) lack of formal guarantees for convergence or incentive compatibility [8].”
A Game Theory Dynamic Incentive Optimization Model for Decentralized Federated Learning
Chen, J. H., Li, D., Wang, P., Liu, P., Zhang, M., & Xian, Y. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582232
“To overcome this, researchers introduced blockchain, leading to frameworks with token-based or reputation-based incentives [15]–[17].”
Empowering Industrial Federated Learning: Blockchain-Based Model Validation and Incentive Mechanism
Jiang, X., Cheng, Y., Yang, J., Song, Z., Yang, Z., & Yao, L. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3711890
“For instance, DeepChain [32] is an auditable federated learning framework based on blockchain.”
Fortress: Multi-Level Secure and Efficient Distributed Learning
Bai, H., He, K., Li, Y., Chen, J. B., Gao, J., & Du, R. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3715844
“D ISTRIBUTED learning enables multiple clients to collaboratively train machine learning models while keeping training data locally, addressing the growing need for privacy-preserving collaborative intelligence [1], [2].”
Al-Humaimeedy, A. S. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1017056
Zhang, X., Wang, W., Zhang, K., & Du, G. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-37274-1_27
Rahmati, M., & Rahmati, N. (2026) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-026-00401-6
“Blockchain-based reputation systems have been proposed to incentivize honest participation in federated learning, rewarding contributors providing high-quality updates while penalizing malicious actors [57].”
Sharda, D., Gupta, D., Kaur, N., & Bansal, R. (2026) · Cureus Journal of Computer Science. · doi:10.7759/s44389-026-00314-5
Li, X., Chen, Y., Wang, B., Zhou, L., & Wu, Z. (2026) · Research Square · doi:10.21203/rs.3.rs-11170656/v1
CB-GFL: carbon-budgeted federated learning with blockchain auditing for healthcare applications
Bhardwaj, T., & Sumangali, K. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1961099
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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