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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Shamir, A. (1979) · Communications of the ACM · doi:10.1145/359168.359176 · Cited by 13,756
Bitcoin: A Peer-to-Peer Electronic Cash SystemPreprint
Squarepants, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3977007 · Cited by 11,146
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
Paillier, P. (2007) · Lecture Notes in Computer Science · doi:10.1007/3-540-48910-x_16 · Cited by 7,250
(2013) · Cited by 5,372
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956
Universally composable security: a new paradigm for cryptographic protocols
Canetti, R. (2001) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2001.959888 · Cited by 3,348
Large Scale Distributed Deep Networks
Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,202
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356
Research works printed but not linked: 4
- goldreich, 2009, Foundations of Cryptography Volume 2 Basic Applications
- pyrgelis, 0, Knock knock, who’s there? membership inference on aggregate location data, Proc 25th Annu Netw Distrib Syst Security Symp NDSS 2018, 1
- 10.1109/TIFS.2017.2787987 · DOI · Paper page
- haykin, 1994, Neural Networks A Comprehensive Foundation
Cited by
Sorted by citations
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565
“A similar blockchain-based model is also introduced in [169] to provide data confidentiality, computation auditability, and incentives for the participants of FL.”
BackgroundA survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407
“The incentive mechanisms may be helpful [79], [80], [185].”
BackgroundA survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083
“…efforts that utilize traditional Privacy-Enhancing Technologies (e.g., zero-knowledge proofs, differential privacy, and federated learning) to tackle privacy challenges posed by LLMs. Exploring additional PETs techniques, whether they are established methods or innovative approaches, to…”
MethodSelf-citationKang, J., Xiong, Z., Niyato, D. T., Xie, S., & Zhang, J. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2940820 · Cited by 961
Self-citationArtificial intelligence for the metaverse: A survey
Huynh‐The, T., Pham, Q., Pham, X., Nguyen, T. T., Han, Z., & Kim, D. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105581 · Cited by 832
Federated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017377 · Cited by 613
“For example, Weng et al. [21] proposed a system called DeepChain for the collaborative learning.”
BackgroundCrowdbc: a blockchain-based decentralized framework for crowdsourcing
Li, M., Weng, J., Yang, A., Lu, W., Zhang, Y., Hou, L., Liu, J.-N., Xiang, Y., & Deng, R. H. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2018.2881735 · Cited by 522
“Blockchain technology provides a new direction for us to reduce the role of the middleman in current society [33].”
BackgroundSelf-citationFederated learning for 6G communications: Challenges, methods, and future directions
Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D. (2020) · China Communications · doi:10.23919/jcc.2020.09.009 · Cited by 464
Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3218527 · Cited by 439
“To further improve the FL system using blockchain, the authors of [133] designed strategies to allow unsuspecting parties to participate in privacy-preserving learning and sharing gradients.”
MethodSelf-citationBiscotti: A blockchain system for private and secure federated learning
Shayan, M., Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3044223 · Cited by 354
Bhushan, B., Khamparia, A., Sagayam, K. M., Sharma, S. K., Ahad, M. A., & Debnath, N. C. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102360 · Cited by 353
Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018) · Applied Sciences · doi:10.3390/app8122663 · Cited by 323
Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing
Wang, Y., Su, Z., Zhang, N., & Benslimane, A. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3014385 · Cited by 323
Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey
Issa, W., Moustafa, N., Turnbull, B., Sohrabi, N., & Tari, Z. (2022) · ACM Computing Surveys · doi:10.1145/3560816 · Cited by 347
“Unlike other algorithms that use data sample size [71, 156] or reputation [64] to verify the quality of local updates, this algorithm uses accuracy as a metric to verify the accuracy of local updates before aggregating the global model.”
MethodBlockchain and Machine Learning for Communications and Networking Systems
Liu, Y., Yu, F. R., Li, X., Ji, H., & Leung, V. C. M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2975911 · Cited by 332
“To address security and privacy issues in DL applications with federated learning, the authors in [168] propose a secure and decen-”
MethodBlockchain-Based Federated Learning for Device Failure Detection in Industrial IoT
Zhang, W., Lu, Q., Yu, Q., Li, Z., Liu, Y., Lo, S. K., Chen, S., Xu, X., & Zhu, L. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3032544 · Cited by 306
“Weng et al. [6] employed incentive mechanism and blockchain transactions to protect the privacy of local gradients, and enable auditability of the training process.”
BackgroundBlockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions
Zhu, J., Cao, J., Saxena, D., Jiang, S., & Ferradi, H. (2023) · ACM Computing Surveys · doi:10.1145/3570953 · Cited by 309
Rodríguez-Barroso, N., Jiménez-López, D., Luzón, M. V., Herrera, F., & Martínez-Cámara, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.011 · Cited by 296
“In contrast to SFL protocols, Blockchain based FL enables a decentralized FL environment without single point of failure risks and improved scalability [153, 154].”
A Secure Federated Learning Framework for 5G Networks
Liu, Y., Peng, J., Kang, J., Iliyasu, A. M., Niyato, D., & El-Latif, A. A. A. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.01.1900525 · Cited by 290
“In a word, the current FL models rely on a trust mechanism, which makes it vulnerable to suffer from poisoning attacks [14].”
BackgroundBlockchain and Federated Learning for Collaborative Intrusion Detection in Vehicular Edge Computing
Liu, H., Zhang, S., Zhang, P., Zhou, X., Shao, X., Pu, G., & Zhang, Y. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3076780 · Cited by 291
“Federated learning is an encrypted and distributed machine learning method that can overcome the problem of data isolated island [19], [20].”
MethodSelf-citationA Survey of Incentive Mechanism Design for Federated Learning
Zhan, Y., Zhang, J., Hong, Z., Wu, L., Li, P., & Guo, S. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2021.3063517 · Cited by 301
Self-citationBinDaaS: Blockchain-Based Deep-Learning as-a-Service in Healthcare 4.0 Applications
Bhattacharya, P., Tanwar, S., Bodkhe, U., Tyagi, S., & Kumar, N. (2019) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2019.2961932 · Cited by 294
Blockchain-enabled Federated Learning: A Survey
Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., & Yearwood, J. (2023) · ACM Computing Surveys · doi:10.1145/3524104 · Cited by 286
“The partially decentralized FL can be achieved by several consensus algorithms, including committee-based ones [127, 129, 134, 136, 140, 163, 181, 182], PoFL [128, 138, 173], PoA [177], and PoQ [155].”
MethodApplications of blockchain in unmanned aerial vehicles: A review
Alladi, T., Chamola, V., Sahu, N., & Guizani, M. (2020) · Vehicular Communications · doi:10.1016/j.vehcom.2020.100249 · Cited by 274
Blockchain Meets Metaverse and Digital Asset Management: A Comprehensive Survey
Truong, V. T., Le, L. B., & Niyato, D. T. (2023) · IEEE Access · doi:10.1109/access.2023.3257029 · Cited by 278
“Weng et al. [149] proposed DeepChain , a privacy-preserving scheme for federated learning, in which privacy is ensured during the gradient collecting process.”
BackgroundTowards Fair and Privacy-Preserving Federated Deep Models
Lyu, L., Yu, J., Nandakumar, K., Li, Y., Ma, X., Jin, J., Yu, H., & Ng, K. S. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2996273 · Cited by 250
“[15], [16] investigated privacy-preserving deep learning on blockchain.”
BackgroundWhen Federated Learning Meets Privacy-Preserving Computation
Chen, J., Yan, H., Liu, Z., Zhang, M., Xiong, H., & Yu, S. (2024) · ACM Computing Surveys · doi:10.1145/3679013 · Cited by 278
FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive
Bao, X., Su, C., Xiong, Y. M., Huang, W., & Hu, Y. (2019) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom.2019.00030 · Cited by 238
“Ref [19] proposes DeepChain which provides secure deep learning training and blockchain-based incentive.”
BackgroundKumar, P., Kumar, R., Gupta, G. P., Tripathi, R., Jolfaei, A., & Islam, A. K. M. N. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.10.002 · Cited by 235
Analysis of Data Management in Blockchain-Based Systems: From Architecture to Governance
Paik, H.-Y., Xu, X., Bandara, H. M. N. D., Lee, S. U., & Lo, S. K. (2019) · IEEE Access · doi:10.1109/access.2019.2961404 · Cited by 229
“Combining on-chain and off-chain data is commonly found in large software systems with a blockchain [19], [20].”
BackgroundVFChain: Enabling Verifiable and Auditable Federated Learning via Blockchain Systems
Peng, Z., Xu, J., Chu, X., Gao, S., Yao, Y., Gu, R., & Tang, Y. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3050781 · Cited by 219
Blockchain-Empowered Decentralized Horizontal Federated Learning for 5G-Enabled UAVs
Feng, C., Liu, B., Yu, K., Goudos, S. K., & Wan, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3116132 · Cited by 213
A review of privacy-preserving techniques for deep learning
Boulemtafes, A., Derhab, A., & Challal, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.041 · Cited by 211
“[47] for example presented “Deepchain” a secure framework for distributed deep learning introducing a value-driven incentive mechanism based on Blockchain in order to address malicious adversaries as well as the lack of incentives for distrustful participants.”
BackgroundA Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications
Zuo, Y., Guo, J., Gao, N., Zhu, Y., Jin, S., & Li, X. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315374 · Cited by 216
“The authors of [174] to encourage parties to collaborate in DL model training and share the obtained local gradients.”
BackgroundMethodA Survey of Blockchain and Intelligent Networking for the Metaverse
Fu, Y., Li, C., Yu, F. R., Luan, T. H., Zhao, P., & Liu, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3222521 · Cited by 184
“Weng et al. [115] used a blockchain-based model to provide FL participants with data confidentiality, computational auditability, and incentives.”
MethodIncentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
Tu, X., Zhu, K., Luong, N. C., Niyato, D. T., Zhang, Y., & Li, J. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3177522 · Cited by 181
“Meanwhile, some works studied the incentive mechanisms tackling the encrypted model parameters such as [19], [157], which provide a promising research direction of incentive mechanism design for FL.”
BackgroundSelf-citationTowards Blockchain-Based Reputation-Aware Federated Learning
Rehman, M. H. U., Salah, K., Damiani, E., & Svetinović, D. (2020) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps50562.2020.9163027 · Cited by 181
“…FL systems, reported in Section II, were deployed in different application domains such as IoT networks [21], handwriting recognition [22], news-feed [23], human activity recognition [24], across entire data pipelines in artificial intelligence applications [16], and distributed…”
BackgroundMethodSecurity and Privacy in IoT Using Machine Learning and Blockchain
Waheed, N., He, X., Ikram, M., Usman, M., Hashmi, S. S., & Usman, M. (2020) · ACM Computing Surveys · doi:10.1145/3417987 · Cited by 203
“[134] Data Privacy General CNN MNIST Accuracy Corda Private BAP∗ U/D/K Shen et al.”
BackgroundFederated Recommendation Systems
Yang, L., Tan, B., Zheng, V. W., Chen, K., & Yang, Q. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_16 · Cited by 191
“Among the existing solutions, [24] proposed the DeepChain as one possible solution, which combines the Blockchain3 and federated learning.”
MethodZhang, Y., Xu, C., Ni, J., Li, H., & Shen, X. (2019) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2019.2923222 · Cited by 170
“The second one is A block can be appended to the blockchain, only if it is accepted by a majority of participants [52], [53].”
BackgroundSelf-citationLo, S. K., Lu, Q., Wang, C., Paik, H.-Y., & Zhu, L. (2022) · ACM Computing Surveys · doi:10.1145/3450288 · Cited by 202
“ture Review on Federated Machine Learning: From A Software Engineering Perspective 111:13 Fig. 6. Research Area Trend The interests in data security (e.g., [19, 49]) and client device security (e.g., [35, 48, 171]) are also significantly high. The data security in federated learning can be understood as the degree to…”
BackgroundMethodInfluentialTan, T., Anees, A., Chen, C. H., Li, S., Xu, X., Li, Z., Xiao, Z., Yang, Y., Lei, X., Ang, M., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00055-8 · Cited by 166
Leng, J., Zhu, X., Huang, Z., Xu, K., Liu, Z., Liu, Q., & Chen, X. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3257172 · Cited by 171
“Few studies involve transfer learning for blockchain data [40], [41].”
BackgroundDecentralized and Collaborative AI on Blockchain
Harris, J. D., & Waggoner, B. (2019) · 2019 IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain.2019.00057 · Cited by 164
“Future work can be done to not submit data directly to the smart contract and instead just submit model updates as discussed in section VII-A2 and in DeepChain [24].”
BackgroundCommunication and computation efficiency in Federated Learning: A survey
Almanifi, O. R. A., Chow, C., Tham, M., Chuah, J. H., & Kanesan, J. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100742 · Cited by 180
Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing
Lu, X., Liao, Y., Lio, P., & Hui, P. (2020) · IEEE Access · doi:10.1109/access.2020.2978082 · Cited by 159
Zhao, F., Zeng, G., & Lu, K. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2952605 · Cited by 146
“Thus, it has successfully applied to various domains [9]–[12], such as network traffic control [9], [10], forecasting [11] and privacy preserving domains [12].”
BackgroundPrivacy-Preserving Aggregation in Federated Learning: A Survey
Liu, Z., Guo, J., Yang, W., Fan, J., Lam, K., & Zhao, J. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3190835 · Cited by 163
Toward On-Device Federated Learning: A Direct Acyclic Graph-Based Blockchain Approach
Cao, M., Zhang, L., & Cao, B. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3105810 · Cited by 151
“Combining blockchain and FL, the authors in [38] propose a DeepChain architecture.”
Security and Privacy for Healthcare Blockchains
Zhang, R., Xue, R., & Liu, L. (2021) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3085913 · Cited by 162
“[50] proposed a privacy-preserving blockchain-based federated learning framework named DeepChain, where the blockchain is used as a medium for”
MethodA privacy preserving framework for federated learning in smart healthcare systems
Wang, W., Li, X., Qiu, X., Zhang, X., Brusić, V., & Zhao, J. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103167 · Cited by 145
Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering
Lu, Q., Zhu, L., Xu, X., Whittle, J., Zowghi, D., & Jacquet, A. (2023) · ACM Computing Surveys · doi:10.1145/3626234 · Cited by 168
“An incentive registry records the rewards that correspond to the AI system’s ethical behavior and outcome of decisions [123, 124], e.”
BackgroundBlockchain Systems, Technologies, and Applications: A Methodology Perspective
Cao, B., Wang, Z., Zhang, L., Feng, D., Peng, M., Zhang, L., & Han, Z. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3204702 · Cited by 144
“Different incentive mechanisms have currently been proposed, such as incentives to participate [72]–[74] and cooperate [75]–[77].”
BackgroundBlockchain-based recommender systems: Applications, challenges and future opportunities
Himeur, Y., Sayed, A., Alsalemi, A., Bensaali, F., Amira, A., Varlamis, I., Eirinaki, M., Sardianos, C., & Dimitrakopoulos, G. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100439 · Cited by 135
Awan, S., Li, F., Luo, B., & Liu, M. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363256 · Cited by 125
Blockchain Intelligence for Internet of Vehicles: Challenges and Solutions
Wang, X., Zhu, H., Ning, Z., Guo, L., & Zhang, Y. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3305312 · Cited by 131
“Third, blockchain motivates users to participate in collaboration with integrity to create trustworthy networks [78].”
BackgroundInfluentialSelf-citationA blockchain based privacy-preserving federated learning scheme for Internet of Vehicles
Wang, N., Yang, W., Wang, X., Wu, L., Guan, Z., Du, X., & Guizani, M. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.05.020 · Cited by 128
BDFL: A Byzantine-Fault-Tolerance Decentralized Federated Learning Method for Autonomous Vehicle
Chen, J.-H., Chen, M.-R., Zeng, G., & Weng, J. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3102121 · Cited by 130
“In contrast with the existing blockchain-basedmethodsforIoV[3],[8],[9],[25],thispaperprovides AV s with the P2P network structure which is completely independent of the blockchain.”
ResultSelf-citationA Blockchain‐Based Federated Learning Method for Smart Healthcare
Chang, Y., Fang, C., & Sun, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4376418 · Cited by 119
“References [18, 21] used Shamir secret sharing and threshold Paillier encryption to protect local gradients, respectively, but both consume large computation overhead.”
MethodHe, X., Chen, Q., Tang, L., Wang, W., & Liu, T. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3200121 · Cited by 130
“Federated learning is a cryptography distributed ML method that can overcome the data island problem to a certain extent [12], [13].”
BackgroundMethodPVD-FL: A Privacy-Preserving and Verifiable Decentralized Federated Learning Framework
Zhao, J., Zhu, H., Wang, F., Lu, R., Liu, Z., & Li, H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3176191 · Cited by 120
Tian, Y., Wang, S., Xiong, J., Bi, R., Zhou, Z., & Bhuiyan, M. Z. A. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3243932 · Cited by 123
“Weng et al. [31] proposed DeepChain, a distributed, secure, and fair DL framework, which provides blockchain-based value-driven incentives that force participants to perform correctly.”
BackgroundLo, S. K., Liu, Y., Lu, Q., Wang, C., Xu, X., Paik, H.-Y., & Zhu, L. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3144450 · Cited by 114
“Blockchain and smart contracts are utilized for federated learning to maintain data integrity with its immutability [10]–[12].”
BackgroundChen, J., Li, K., & Yu, P. S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3105682 · Cited by 116
Rathore, S., & Park, J. H. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3040968 · Cited by 113
“Recently, Weng et al. [25] introducedDeepChain;aprivacy-preservingdistributedDLwith blockchaintechnologywhereinincentivemechanismandcryp-tographicprimitives(i.e.,ahomomorphicencryptiontechnique, zero-knowledgeproof)areusedtoachievecomputationau-ditabilityanddataconfidentialityindistributedDL.”
Mechanism Design for An Incentive-aware Blockchain-enabled Federated Learning Platform
Toyoda, K., & Zhang, A. N. (2019) · 2019 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata47090.2019.9006344 · Cited by 118
“Weng et al. proposed a blockchain-enabled privacy-preserving deep learning platform termed DeepChain [2].”
BackgroundInfluentialYazdinejad, A., Dehghantanha, A., Srivastava, G., Karimipour, H., & Parizi, R. M. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103088 · Cited by 115
Lu, K., Zeng, G.-Q., Luo, X., Weng, J., Luo, W., & Wu, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3053304 · Cited by 113
Self-citationAdaptive Privacy-Preserving Federated Learning for Fault Diagnosis in Internet of Ships
Zhang, Z., Guan, C., Chen, H., Yang, X., Gong, W., & Yang, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3115817 · Cited by 110
Differential privacy in blockchain technology: A futuristic approach
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2020.06.003 · Cited by 115
A survey on recent optimal techniques for securing unmanned aerial vehicles applications
Syed, F., Gupta, S. K., Alsamhi, S. H., Rashid, M. U., & Liu, X. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4133 · Cited by 109
Architectural patterns for the design of federated learning systems
Lo, S. K., Lu, Q., Zhu, L., Paik, H.-Y., Xu, X., & Wang, C. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111357 · Cited by 109
A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends
Neto, H. N. C., Hribar, J., Dusparić, I., Mattos, D. M. F., & Fernandes, N. C. (2023) · IEEE Access · doi:10.1109/access.2023.3269980 · Cited by 114
“A similar model, also based on blockchain, is introduced by Weng et al., aiming to provide data confidentiality, computational auditability, and incentives for participants in FL [81].”
MethodDecentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review
Witt, L., Heyer, M., Toyoda, K., Samek, W., & Li, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3231363 · Cited by 114
“Many works, therefore, focus on solving specific issues, such as enhanced privacy [19], [20], [56], [66], novel BC systems [25], [51], bandwidth reduction [68], novel contribution measurements [22], [25], [68], or game theory (e.”
BackgroundMethodInfluentialBlockchain-Based Charging Coordination Mechanism for Smart Grid Energy Storage Units
Baza, M., Nabil, M., Ismail, M., Mahmoud, M. M. E. A., Serpedin, E., & Rahman, M. A. (2019) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain.2019.00076 · Cited by 96
“Recently, blockchains have attracted the attention of both academia and industry across a wide range of fields [8], [9].”
BackgroundBlockchain-based federated learning methodologies in smart environments
Li, D., Luo, Z., & Cao, B. (2021) · Cluster Computing · doi:10.1007/s10586-021-03424-y · Cited by 105
“In [62], authors provided a new model based on a deeply distributed, safe, and equitable framework called the Deep chain is a value-based incentive mechanism that strengthens the motivation to participate.”
BackgroundChronos+: An Accurate Blockchain-based Time-stamping Scheme for Cloud Storage
Zhang, Y., Xu, C., Cheng, N., Li, H., Yang, H., & Shen, X. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2947476 · Cited by 94
“blockchains have been envisioned as a powerful tool to enhance cloud storage services in terms of security and efficiency [30], [31], [32], [33], [34], [35].”
MethodSelf-citationIncentive techniques for the Internet of Things: A survey
Maddikunta, P. K. R., Pham, Q., Nguyen, D. C., Huynh‐The, T., Aouedi, O., Yenduri, G., Bhattacharya, S., & Gadekallu, T. R. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103464 · Cited by 97
Deep reinforcement learning for blockchain in industrial IoT: A survey
Wu, Y., Wang, Z., Ma, Y., & Leung, V. C. M. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108004 · Cited by 92
Making Smart Contracts Smarter
Badruddoja, S., Dantu, R., He, Y., Upadhyay, K., & Thompson, M. (2021) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc51069.2021.9461148 · Cited by 90
Tariq, A., Serhani, M. A., Sallabi, F., Barka, E. S., Qayyum, T., Khater, H. M., & Shuaib, K. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3438264 · Cited by 130
Zerka, F., Urovi, V., Vaidyanathan, A., Barakat, S., Leijenaar, R. T. H., Walsh, S., Gabrani-Juma, H., Miraglio, B., Woodruff, H. C., Dumontier, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3029445 · Cited by 89
“Weng et al. proposed DeepChain, an optimized blockchain for secure distributed deep learning training [20], however the weights are saved directly to the blockchain and are accessible by all the partners within the distributed network.”
BackgroundThe role of blockchain in reducing the impact of barriers to humanitarian supply chain management
Ozdemir, A., Erol, I., Ar, I. M., Peker, I., Asgary, A., Medeni, T., & Medeni, I. T. (2020) · The International Journal of Logistics Management · doi:10.1108/ijlm-01-2020-0058 · Cited by 65
FRUIT: A Blockchain-Based Efficient and Privacy-Preserving Quality-Aware Incentive Scheme
Zhang, C., Zhao, M., Zhu, L., Zhang, W., Wu, T., & Ni, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3213341 · Cited by 87
“Blockchain, as an emerging technique that has the properties of transparency and immutability, has received increasing attention [5], [6], [7] and has been widely applied to design incentive schemes [8], [9], [10], [11], [12].”
MethodRecord and Reward Federated Learning Contributions with Blockchain
Martinez, I., Francis, S., & Hafid, A. S. (2019) · 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc.2019.00018 · Cited by 92
“Training Model privacy is achieved through the use of Paillier’s Cryptosystem, a homomorphic encryption scheme commonly used in distributed machine learning [4]; gradient privacy is achieved by uploading the full gradient to an off-chain table on IPFS only accessible by O as defined in Section II-A.”
BackgroundMethodWang, W., Wang, Y., Duan, P., Liu, T., Tong, X., & Cai, Z. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3187047 · Cited by 85
“[39] proposed a distributed, secure and”
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey
Jiang, Y., Ma, B., Wang, X., Yu, G., Yu, P., Wang, Z., Ni, W., & Liu, R. P. (2024) · ACM Computing Surveys · doi:10.1145/3659099 · Cited by 97
“Weng et al. [94] propose a DeepChain framework with a value-driven incentive mechanism to force the participants to train the model following the rules.”
BackgroundPrivacy-preserving in Blockchain-based Federated Learning systems
Sameera, K. M., Nicolazzo, S., Arazzi, M., Nocera, A., A., R. R. K., Vinod, P., & Conti, M. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.04.024 · Cited by 107
AI on the edge: a comprehensive review
Su, W., Li, L., Liu, F., He, M., & Liang, X. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10141-4 · Cited by 90
“To alleviate these problems, some of the existing works discard PS, which can be achieved by blockchain (Weng et al. 2021; Kim et al. 2019; Lu et al. 2019; Zhao et al. 2020) and other communication methods (Lalitha et al.”
BackgroundHiwale, M., Walambe, R. A., Potdar, V. M., & Kotecha, K. V. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100192 · Cited by 94
Toward Blockchain-Based Fair and Anonymous Ad Dissemination in Vehicular Networks
Li, M., Weng, J., Yang, A., Liu, J.-N., & Lin, X. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2940148 · Cited by 82
Self-citationOptimizing Task Assignment for Reliable Blockchain-Empowered Federated Edge Learning
Kang, J., Xiong, Z., Li, X., Zhang, Y., Niyato, D., Leung, C., & Miao, C. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3055767 · Cited by 81
“Similarly, the authors in [8] design a secure and fair blockchain-based federated learning framework to solve security problems of data confidentiality and computation auditability.”
BackgroundMethodInfluentialSelf-citationInternet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges
Tang, Q., Yu, F. R., Xie, R., Boukerche, A., Huang, T., & Liu, Y. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3175453 · Cited by 81
“Blockchain has also been widely adopted to guarantee reliable and trustworthy incentives [259], [262], [263].”
MethodAn Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
Xu, C., Qu, Y., Luan, T. H., Eklund, P., Xiang, Y., & Gao, L. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3232603 · Cited by 77
“The blockchain is a motivation for the nodes to participate in FL and contribute high-quality local models [19], [20], [27], a distributed reputation management system to resist repudiation and tampering [26], and an auditable distributed database that allows FL to conduct a transparent training process [21].”
BackgroundStephanie, V., Khalil, I., Atiquzzaman, M., & Yi, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3214998 · Cited by 82
“Similarly, authors in [21] presented an incentive-driven mechanism in blockchain for FL called DeepChain.”
Free-riders in Federated Learning: Attacks and Defenses
Lin, J., Du, M., & Liu, J. (2019) · arXiv · doi:10.48550/arxiv.1911.12560 · Cited by 76
“Although the profit model [7] for federated learning has not been fully developed, one would assume that a client who contributes data to the model training process could either get money or the privilege to use the final trained model as a reward.”
MethodA survey for trust-aware recommender systems: A deep learning perspective
Dong, M., Yuan, F., Yao, L., Wang, X., Xu, X., & Zhu, L. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108954 · Cited by 93
Shen, M., Wang, H., Zhang, B., Zhu, L., Xu, K., Li, Q. L., & Du, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028110 · Cited by 73
“With the advanced features such as anonymity and traceability, blockchain has emerged as a promising technology to provide distributed secure solutions in FL which provides a guaranteed collaborative scheme among untrusted participants and servers for efficient model training [4], [7]–[9].”
Background
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
