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
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
Items 101–200 of 610
A Privacy-Preserving and Reputation-Based Truth Discovery Framework in Mobile Crowdsensing
Cheng, Y., Ma, J., Liu, Z., Li, Z., Wu, Y., Dong, C., & Li, R. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3276976 · Cited by 74
“Meanwhile, in light of the relationship between the reliability of mobile users and the accuracy of the ground truths of sensing tasks, the committee-generated or leader-selected method in blockchain [28], [29], [30] can be used, which introduces a concept of sub-user.”
MethodLakhan, A. R., Mohammed, M. A., Kozlov, S. A., & Rodrigues, J. J. P. C. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4363 · Cited by 72
“These methods are dynamic scheduling methods and preemptive methods and are widely implemented in References 10 and 11.”
MethodInfluentialBlockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing
Yao, S. F., Wang, M., Qu, Q., Zhang, Z., Zhang, Y.-F., Xu, K., & Xu, M. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3213358 · Cited by 72
“Some efforts such as [16] apply the blockchain to solve the data security problems in distributed learning systems.”
BackgroundSelf-citationSecurity and privacy-aware Artificial Intrusion Detection System using Federated Machine Learning
Kumar, K. P. S., Nair, S., Roy, D. G., Rajalingam, B., & Kumar, R. S. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107440 · Cited by 73
Bift: A Blockchain-Based Federated Learning System for Connected and Autonomous Vehicles
He, Y., Huang, K., Zhang, G., Yu, F. R., Chen, J., & Li, J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3135342 · Cited by 70
“DeepChain [50] provides secure deep learning training and blockchain-based incentive.”
BackgroundBlockchain-Based Federated Learning: A Survey and New Perspectives
Ning, W., Zhu, Y., Song, C., Li, H., Zhu, L., Xie, J., Chen, T., Xu, T., Xi, X., & Gao, J. (2024) · Applied Sciences · doi:10.3390/app14209459 · Cited by 78
“DeepChain [49] provides blockchain-based value-driven incentives to force participants to behave correctly.”
CrowdSFL: A Secure Crowd Computing Framework Based on Blockchain and Federated Learning
Li, Z., Liu, J., Hao, J., Wang, H., & Xian, M. (2020) · Electronics · doi:10.3390/electronics9050773 · Cited by 72
“[28] proposed the DeepChain, which gives mistrustful parties incentives to participate in privacy-preserving learning, share gradients, and update parameters correctly, and eventually accomplish iterative deep-learning with a win-win result.”
BackgroundIncentive Mechanisms in Federated Learning and A Game-Theoretical Approach
Zeng, R., Zeng, C. L., Wang, X., Li, B., & Chu, X. (2022) · IEEE Network · doi:10.1109/mnet.112.2100706 · Cited by 68
Blockchain-Supported Federated Learning for Trustworthy Vehicular Networks
Otoum, S., Al Ridhawi, I., & Mouftah, H. T. (2020) · GLOBECOM 2020 - 2020 IEEE Global Communications Conference · doi:10.1109/globecom42002.2020.9322159 · Cited by 92
“Recently, many articles have discussed opportunities and benefits of the integration of Blockchain and FL technologies [8] [9] [10] [11].”
BackgroundMethodEnhancing privacy preservation and trustworthiness for decentralized federated learning
Wang, L., Zhao, X., Lu, Z., Wang, L., & Zhang, S. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.130 · Cited by 68
A Comprehensive Survey of Incentive Mechanism for Federated LearningPreprint
Zeng, R., Zeng, C. L., Wang, X., Li, B., & Chu, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15406 · Cited by 112
“In [45], Weng points out two serious problems of security threats from dishonest behaviors and the deficiency of incentive component in FL.”
Zhang, Z., Xu, X., Gong, W., Chen, Y., & Gao, H. (2021) · Control Engineering Practice · doi:10.1016/j.conengprac.2021.104913 · Cited by 66
A Systematic Survey of Blockchained Federated LearningPreprint
Wang, Z., Hu, Q., Xu, M., Zhuang, Y., Wang, Y., & Cheng, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02182 · Cited by 103
“The work in [49] designs a payment-based incentive mechanism to encourage participates to collaboratively train a deep learning model.”
BackgroundFGFL: A blockchain-based fair incentive governor for Federated Learning
Gao, L., Li, L., Chen, Y., Xu, C., & Xu, M. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.01.019 · Cited by 66
Akrami, N. E., Hanine, M., Flores, E. S., Aray, D. G., & Ashraf, I. (2023) · IEEE Access · doi:10.1109/access.2023.3298371 · Cited by 63
Hou, D., Zhang, J., Man, K. L., Ma, J., & Peng, Z. (2021) · Information Communication Technologies Conference (ICTC) · doi:10.1109/ictc51749.2021.9441499 · Cited by 62
“Besides, homomorphic encryption is applied in [11] [12].”
BackgroundMethodInfluentialSelf-citationChen, Y., Zhang, Y., Wang, S., Wang, F., Li, Y., Jiang, Y., Chen, L., & Guo, B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3191671 · Cited by 62
“Current incentive mechanisms can be divided into three types: 1) reputation-based [4], [10]; 2) payment-based [5], [6], [11], and 3) noncooperative game-based approaches (also called “tit-for-tat” approaches) [7], [8], [13].”
BackgroundSelf-citationFadi, O., Zkik, K., Ghazi, A. E., & Boulmalf, M. (2022) · IEEE Access · doi:10.1109/access.2022.3203568 · Cited by 71
“…they predict the category of 901 VOLUME 10, 2022 Other approaches adopted deep learning methods to pre-959 serve blockchain privacy; the authors in [84] developed the framework DeepChain that guarantees data privacy for each participant and provides auditability for the whole training process…”
BackgroundSecure and Efficient Federated Learning Through Layering and Sharding Blockchain
Yuan, S., Cao, B., Sun, Y., Wan, Z., & Peng, M. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3361458 · Cited by 67
“15 and 16. the basic iteration model from the DAG-based mainchain, which incorporates models from all shards.”
BackgroundMethodInfluentialSVeriFL: Successive verifiable federated learning with privacy-preserving
Gao, H., He, N., & Gao, T. (2022) · Information Sciences · doi:10.1016/j.ins.2022.11.124 · Cited by 61
Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions
Zhu, C., Xiao, Z., Ren, J., & Qin, T. (2022) · IEEE Access · doi:10.1109/access.2022.3174865 · Cited by 60
“DeepChain also provides a blockchain-based value-driven incentive mechanism to force participants to behave appropriately [96].”
BackgroundLightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning
Jin, R. M., Hu, J. W., Min, G., & Mills, J. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3293731 · Cited by 62
“…shedding new light on handling the single point of failure in conventional FL. Research works [7], [8], [9], [10], [12], [14], [17], [18], [19], [20], [21] move the aggregation step from the server to the blockchain nodes, while Qu et al. [22] and Mugunthan et al. [15] eliminate the role of the…”
Faithful Edge Federated Learning: Scalability and Privacy
Zhang, M., Wei, E., & Berry, R. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118423 · Cited by 61
[Retracted] Blockchain‐Based Deep Learning to Process IoT Data Acquisition in Cognitive Data
Hannah, S., Deepa, A. J., Chooralil, V. S., Sangeetha, S., Natarajan, Y., Raja, R. A., Suresh, C. P., Vignesh, R., YasirAbdullahR, Srihari, K., et al. (2022) · BioMed Research International · doi:10.1155/2022/5038851 · Cited by 58
“…normal aging and the start MCI, the transitional aging and dementia, which is cognitive abnormalities but generally living, have piqued the interest of from a reactive to a pro-active on health management rather than disease factor in these cost savings in case of cognitive like Alzheimer Disease.”
RetractedData-Centric Client Selection for Federated Learning Over Distributed Edge Networks
Saha, R., Misra, S., Chakraborty, A., Chatterjee, C., & Deb, P. K. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3217271 · Cited by 58
“Additionally, Bao et al. [30] and Weng et al. [31] proposed blockchain-based FL with incentive registry to record the performance of each client and provide incentives to increase client’s participation.”
BackgroundZeroCross: A sidechain-based privacy-preserving Cross-chain solution for Monero
Li, Y., Weng, J., Li, M., Wu, W., Weng, J., Liu, J.-N., & Hu, S. (2022) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2022.07.008 · Cited by 57
Self-citationPrivacy-preserving Decentralized Learning Framework for Healthcare System
Kasyap, H., & Tripathy, S. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3426474 · Cited by 56
Blockchain-Enabled Federated Learning With Mechanism Design
Toyoda, K., Zhao, J., Zhang, A. N. S., & Mathiopoulos, P. T. (2020) · IEEE Access · doi:10.1109/access.2020.3043037 · Cited by 59
“Some existing methods penalize dishonest workers, those who deteriorate the quality of models (e.g. [2]).”
BackgroundMethodInfluentialBlockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing
Hu, Q., Wang, Z., Xu, M., & Cheng, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3128155 · Cited by 56
“[39] devised DeepChain to achieve auditability of the whole FL”
MethodzkFL: Zero-Knowledge Proof-Based Gradient Aggregation for Federated Learning
Wang, Z., Dong, N., Sun, J., Knottenbelt, W. J., & Guo, Y. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3403370 · Cited by 72
“Blockchain has been integrated with FL to tackle the security and privacy issues in existing FL [24], [25], [26], [27], [28].”
A framework for the prediction of earthquake using federated learning
Tehseen, R., Farooq, M. S., & Abid, A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.540 · Cited by 52
“Some researchers have integrated FL with block chain technology (Kim & Hong, 2019; Farooq, Khan & Abid, 2020; Majeed & Hong, 2019; Huang et al., 2020; Weng et al., 2019).”
BackgroundA privacy-preserving and verifiable federated learning method based on blockchain
Chen, F., Guo, Y., Ma, J., Xie, H., & Wang, Y. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.01.002 · Cited by 52
Alam, T., Ullah, A., & Benaida, M. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03663-2 · Cited by 52
“1 3 suggest the DRL decision-making methodology focused on all entities, such that the informants can begin to collaborate in blockchain networks in a cross-environment while encouraging the innovation despite unpredictable restrictions (Nguyen et al. 2019; Qiu et al. 2019; Weng et al. 2019).”
BackgroundA survey on blockchain-enabled federated learning and its prospects with digital twin
Liu, K., Yan, Z., Liang, X., Kantola, R., & Hu, C. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.08.001 · Cited by 52
“Table 1 shows that most of the papers do not punish malicious trainers severely to meet the IDF requirement, except [34,57].”
BackgroundInfluentialDeepRing: Protecting Deep Neural Network With Blockchain
Goel, A., Agarwal, A., Mayank Vatsa, A., Singh, R., & Ratha, N. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00341 · Cited by 50
“For example, a recent algorithm, DeepChain, is proposed which is a collaborative training in a distributed, secure and decentralized environment [22].”
MethodBlockchain for Transparent Data Management Toward 6G
Shen, X., Liu, D., Huang, C., Xue, L., Yin, H., Zhuang, W., Sun, R., & Ying, B. (2021) · Engineering · doi:10.1016/j.eng.2021.10.002 · Cited by 54
“[104] to securely compute model parameter updates with a threshold Paillier algorithm.”
MethodBlockchain-Enabled: Multi-Layered Security Federated Learning Platform for Preserving Data Privacy
Mahmood, Z., & Jusas, V. (2022) · Electronics · doi:10.3390/electronics11101624 · Cited by 51
“First, previous studies such as [43,52,53] have focused primarily on how Blockchain can implement FL systems.”
BackgroundWang, W., Wang, Y., Huang, Y., Mu, C., Sun, Z., Tong, X., & Cai, Z. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109206 · Cited by 51
Heterogeneous Differential-Private Federated Learning: Trading Privacy for Utility Truthfully
Lin, X., Wu, J., Li, J., Sang, C., Hu, S., & Deen, M. J. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3241057 · Cited by 57
“Federated learning with incentive mechanism has been studied extensively [18], [19], [20], [21], [22], [23].”
BackgroundVCD-FL: Verifiable, Collusion-Resistant, and Dynamic Federated Learning
Gao, S., Luo, J., Zhu, J., Dong, X., & Shi, W. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3271268 · Cited by 53
“Regarding this issue, most existing works that focus on verifiable FL aim to solve the problems such as privacy [18], [19], [29], performance [20], and auditability [21], [30].”
InfluentialChang, S. E., & Chen, Y. (2020) · Journal of Medical Internet Research · doi:10.2196/19480 · Cited by 60
Towards a Secure and Reliable Federated Learning using Blockchain
Moudoud, H., Cherkaoui, S., & Khoukhi, L. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685388 · Cited by 50
“In [22], the authors proposed DeepChain, a secure and reliable FL framework that improves the reliability of FL devices using blockchain smart contracts.”
Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis
Alghamdi, A., Zhu, J., Yin, G., Shorfuzzaman, M., Alsufyani, N., AlYami, S. S., & Biswas, S. (2022) · Sensors · doi:10.3390/s22186786 · Cited by 51
“For example, Deep-Chain proposed for collaborative learning in [17].”
BackgroundDRLBTS: deep reinforcement learning-aware blockchain-based healthcare system
Lakhan, A. R., Mohammed, M. A., Nedoma, J., Martínek, R., Tiwari, P., & Kumar, N. (2023) · Scientific Reports · doi:10.1038/s41598-023-29170-2 · Cited by 55
“Equation (12) determines the optimal policy of the workflows in different network states.”
BackgroundPrivacy reinforcement learning for faults detection in the smart grid
Belhadi, A., Djenouri, Y., Srivastava, G., Jolfaei, A., & Lin, J. C. (2021) · Ad Hoc Networks · doi:10.1016/j.adhoc.2021.102541 · Cited by 48
FabricFL: Blockchain-in-the-Loop Federated Learning for Trusted Decentralized Systems
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Dehghantanha, A., & Choo, K. R. (2021) · IEEE Systems Journal · doi:10.1109/jsyst.2021.3124513 · Cited by 51
“research work proposed in [14]; the model owner initiates the process by uploading the model and registering the model and training description to orchestrator smart contract.”
MethodApplying Federated Learning in Software-Defined Networks: A Survey
Ma, X., Liao, L., Li, Z., Lai, R. X., & Zhang, M. (2022) · Symmetry · doi:10.3390/sym14020195 · Cited by 50
“…contract theory [65] decentralized incentive [66,67] based on data and work [68,69] dynamic renewal contract [70–72] multi-dimensional information [73] value-driven incentive [74] built on Ethereum fairness theory [75,76] reputation incentive protocol [77–79] reporting costs truthfully [76]…”
BackgroundMethodCommunication-Efficient Federated Learning
Kishor, K. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-85559-8_9 · Cited by 49
A systematic review of federated learning from clients’ perspective: challenges and solutions
Shanmugarasa, Y., Paik, H.-Y., Kanhere, S. S., & Zhu, L. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10563-8 · Cited by 55
Tong, Z., Wang, J., Hou, X., Chen, J., Jiao, Z., & Liu, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3370964 · Cited by 47
“Weng et al. [29] utilized an underlying incentive mechanism based on blockchain to guarantee fairness.”
BackgroundDefending Against Poisoning Attacks in Federated Learning With Blockchain
Dong, N., Wang, Z., Sun, J., Kampffmeyer, M., Knottenbelt, W. J., & Xing, E. P. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3376651 · Cited by 52
“Current blockchain-based FL designs [8], [35], [36], [37], [38] have been broadly used in diverse fields [39], [40], [41].”
BackgroundAdvancing Blockchain-based Federated Learning through Verifiable Off-chain Computations
Heiss, J., Grünewald, E., Tai, S., Haimerl, N., & Schulte, S. (2022) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain55522.2022.00034 · Cited by 46
BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework
Qin, Z., Yan, X., Zhou, M., & Deng, S. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645425 · Cited by 55
“Second, some of the technical selections hurt FL, e.g., although DP can protect privacy with the guarantees of mathematical proof from the perspective of data reconstruction and membership inference, etc., it imposes a significant loss of accuracy for protecting complicated models [38].”
BackgroundInfluentialUsing Blockchain Technologies to Improve Security in Federated Learning Systems
Short, A. R., Leligou, H. C., Papoutsidakis, M., & Theocharis, E. (2020) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.00-96 · Cited by 47
Block-healthnet: security based healthcare system using block-chain technology
Shanthapriya, R., & Vaithianathan, V. (2020) · Security Journal · doi:10.1057/s41284-020-00265-z · Cited by 45
“Weng et al. (2019) proposed deep learning framework and block-chain based incentive for auditing and privacy-preserving.”
BackgroundThe Different Faces of AI Ethics Across the World: A Principle-to-Practice Gap Analysis
Tidjon, L. N., & Khomh, F. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3225132 · Cited by 79
“…[127], [141], [142], [143], [144], [145], [146], explain-ability [147], [148], [149], interpretability [147], [148], [149], transparency and accountability [147], [148], [149], [150], traceability and auditability [129], [151], [152], and trustworthy (especially formal verification [130]).”
MethodA Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture
Vimalajeewa, D., Kulatunga, C., Berry, D. P., & Balasubramaniam, S. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2020.3048671 · Cited by 44
Framework for determining the suitability of blockchain: Criteria and issues to consider
Hassija, V., Zeadally, S., Jain, I., Tahiliani, A., Chamola, V., & Gupta, S. R. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4334 · Cited by 44
Lu, Q., Zhu, L., Xu, X., & Whittle, J. (2023) · IEEE Software · doi:10.1109/ms.2022.3233582 · Cited by 43
“When executing a task, an incentive registry records the rewards that are given for the decisions and behaviors of AI systems, 21 e.g., rewards for the recommended path without safety risk.”
MethodFedBC: Blockchain-based Decentralized Federated Learning
Wu, X. G., Wang, Z., Zhao, J., Zhang, Y., & Wu, Y. (2020) · IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) · doi:10.1109/icaica50127.2020.9182705 · Cited by 43
“In our main experiment, we set the number of participating nodes to 10, which is often used in experiments in the existing literature [21].”
MethodSelf-citationZhang, Q., Ding, Q., Zhu, J., & Li, D. (2021) · IEEE Wireless Communications and Networking Conference Workshops (WCNCW) · doi:10.1109/wcncw49093.2021.9420026 · Cited by 43
“DeepChain [18] is a deep learning framework based on blockchain incentive mechanism, which rewards the workers through the Deepcoin that can be used in platform, so as to improve the activity of worker.”
BackgroundGreat partners: how deep learning and blockchain help improve business operations together
Luo, S., & Choi, T. (2021) · Annals of Operations Research · doi:10.1007/s10479-021-04101-4 · Cited by 45
Motaharifar, M., Norouzzadeh, A., Abdi, P., Iranfar, A., Lotfi, F., Moshiri, B., Lashay, A., Mohammadi, S.-F., & Taghirad, H. D. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.612949 · Cited by 46
“It is undeniable that there is a huge potential in employing deep learning methods in a wide range of various applications Weng et al. (2019), Antoniades et al. (2016), Lotfi et al. (2018), Lotfi et al. (2020).”
BackgroundA blockchain-based audit approach for encrypted data in federated learning
Sun, Z., Wan, J., Yin, L., Cao, Z., Luo, T., & Wang, B. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.05.006 · Cited by 45
IoMT: A Medical Resource Management System Using Edge Empowered Blockchain Federated Learning
Muazu, T., Mao, Y., Muhammad, A. U., bin Ibrahim, M., Samuel, O., & Tiwari, P. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3308331 · Cited by 42
“Furthermore, recent works have leveraged blockchain technology for distributed data sharing, security, and privacy, such as [7], [9], [10], [11], [12], [13], [14], [15], [16].”
BackgroundA Secure and Efficient Blockchain Sharding Scheme via Hybrid Consensus and Dynamic Management
Li, M., Luo, X., Xue, K., Xue, Y., Sun, W., & Li, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3406145 · Cited by 42
“The applications of blockchain span across diverse sectors, such as health-care [2], [3], [4], [5], federated learning [6], [7], cloud computing [8], [9], mobile networks [10], [11], and the Internet of Things [12], [13], [14], [15].”
BackgroundSelf-citationA Survey on Blockchain-Based Federated Learning
Wu, L., Ruan, W., Hu, J., & He, Y. (2023) · Future Internet · doi:10.3390/fi15120400 · Cited by 48
A Privacy-Preserving and Redactable Healthcare Blockchain System
Xu, S., Ning, J., Li, X., Yuan, J., Huang, X., & Deng, R. H. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3356595 · Cited by 41
“While so far most of the focus has been on the financial industries [2], [3], several projects in other services-related areas such as medical industries.”
BackgroundPeyvandi, A., Majidi, B., Peyvandi, S., & Patra, J. C. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12900-5 · Cited by 43
“In this paper, federated learning [63] combined with blockchain is used to address the issue of privacy preservation in MLaaS.”
BackgroundMethodInfluentialEnhancing Data Privacy: A Comprehensive Survey of Privacy-Enabling Technologies
Razi, Q., Piyush, R., Chakrabarti, A., Singh, A., Hassija, V., & Chalapathi, G. (2025) · IEEE Access · doi:10.1109/access.2025.3546618 · Cited by 44
“While this approach provides stronger and more reliable privacy protections without depending on users understanding legal policies, it can be more complex and expensive to implement [21], [22].”
The Applications of Blockchain in Artificial Intelligence
Wang, R., Luo, M., Wen, Y., Wang, L., Choo, K. R., & He, D. (2021) · Security and Communication Networks · doi:10.1155/2021/6126247 · Cited by 43
Permissioned Blockchain Frame for Secure Federated Learning
Sun, J. D., Wu, Y. N., Wang, S., Fu, Y., & Chang, X. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3121297 · Cited by 40
Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments
Djenouri, Y., Srivastava, G., Belhadi, A., & Lin, J. C. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4332 · Cited by 39
“Weng et al 18 invented DeepChain, a novel framework which can be defined as a distributed DL (deep learning) system that can be used for solving FL (federated learning) issues.”
BackgroundA Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods
Khajehali, N., Yan, J. H., Chow, Y.-W., & Fahmideh, M. (2023) · Sensors · doi:10.3390/s23167235 · Cited by 41
Privacy-preserving and Byzantine-robust Federated Learning Framework using Permissioned Blockchain
Kasyap, H., & Tripathy, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122210 · Cited by 41
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)
Lugan, S., Desbordes, P., Brion, E., Ramos Tormo, L. X., Legay, A., & Macq, B. (2019) · IEEE Access · doi:10.1109/access.2019.2959220 · Cited by 39
Mao, Q., Wang, L., Yu, L., Han, L., Wang, Z., & Chen, K. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111338 · Cited by 40
Threats and Defenses in the Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Li, Y., Guo, Z., Yang, N., Chen, H., Yuan, D., & Ding, W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3563537 · Cited by 38
“Studies [54], [55] investigate dishonest reporting during intensive distribution.”
Blockchain-Based Federated Learning in Medicine
El-Rifai, O., Biotteau, M., Boissezon, X. D., Megdiche, I., Ravat, F., & Teste, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59137-3_20 · Cited by 40
Towards Blockchain-Based Federated Machine Learning: Smart Contract for Model Inference
Drungilas, V., Vaičiukynas, E., Jurgelaitis, M., Butkienė, R., & Čeponienė, L. (2021) · Applied Sciences · doi:10.3390/app11031010 · Cited by 38
“[8] Algorand 1 Yes Corda Yes Deep learning Permissioned”
BackgroundInfluentialBPFL: Blockchain-based privacy-preserving federated learning against poisoning attack
Ren, Y., Hu, M., Yang, Z., Feng, G., & Zhang, X. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120377 · Cited by 34
A Blockchain-Enabled Federated Learning Model for Privacy Preservation: System Design
Qi, M., Wang, Z., Wu, F., Hanson, R., Chen, S., Xiang, Y., & Zhu, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90567-5_24 · Cited by 37
μDFL: A Secure Microchained Decentralized Federated Learning Fabric Atop IoT Networks
Xu, R., & Chen, Y. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3179892 · Cited by 40
“…research on decentralized federated learning (DFL) mostly focuses on combination of cryptographic schemes to ensure data confidentiality and privacy in distributed model training process [9], [21] or incentive mechanisms for fair rewards and verifiable FL models by the edge computing [15], [32].”
BackgroundMethodInfluentialPrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning
Wang, D., Ren, J., Wang, Z., Wang, Y., & Zhang, Y. (2022) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3230904 · Cited by 36
“Weng et al. [17] presented a blockchain-based and data-driven incentive mechanism to force participants to behave correctly, and provide data privacy guarantees by cryptographic primitives.”
BackgroundSelf-citationExploiting Blockchain to Make AI Trustworthy: A Software Development Lifecycle View
Zhang, P., Ding, S., & Zhao, Q. (2023) · ACM Computing Surveys · doi:10.1145/3614424 · Cited by 46
“Text mining/classification [29] Robustness Redefine and analyze security problems for transfer learning cases.”
BackgroundDigital Twin-Assisted Federated Learning Service Provisioning Over Mobile Edge Networks
Zhang, R., Xie, Z., Yu, D., Liang, W., & Cheng, X. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3337317 · Cited by 36
“In [35], Weng et al. introduced an auditable and privacy-preserving deep learning framework that incorporates a value-driven incentive mechanism based on blockchain technology.”
BackgroundNarula, M., Meena, J., & Vishwakarma, D. K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108128 · Cited by 38
Alabadi, M., Habbal, A., & Guizani, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3372375 · Cited by 36
“In [25], the article presents the DeepChain framework, which can securely collect local intermediate gradients from untrustworthy parties by launching transactions.”
BackgroundMethodStrategies for Enhancing Training and Privacy in Blockchain Enabled Federated Learning
Kumar, S., Dutta, S., Chatturvedi, S., & Bhatia, M. P. S. (2020) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm50055.2020.00058 · Cited by 36
“DeepChain is a popular work that introduces the concept of workers, which perform the task of updating the model [25].”
BackgroundLightweight Privacy and Security Computing for Blockchained Federated Learning in IoT
Fan, M., Ji, K., Zhang, Z., Yu, H., & Sun, G. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3267112 · Cited by 37
“[13], our LPBFL can achieve a large universe construction, which is more suitable for blockchain-based federated learning systems.”
BackgroundMethodResultInfluentialXin, Y., Cai, P.-C., Li, P., Yan, Q., Sun, Y., Ding, Q., Cheng, S.-L., & Zhao, Q. (2023) · Physica B Condensed Matter · doi:10.1016/j.physb.2023.415157 · Cited by 35
Rahmadika, S., Firdaus, M., Jang, S., & Rhee, K.-H. (2021) · Security and Communication Networks · doi:10.1155/2021/5550153 · Cited by 35
FLRA: A Reference Architecture for Federated Learning Systems
Lo, S. K., Lu, Q., Paik, H.-Y., & Zhu, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86044-8_6 · Cited by 33
Towards Secure and Fair IIoT-Enabled Supply Chain Management via Blockchain-Based Smart Contracts
Alahmadi, A., & Lin, X. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761216 · Cited by 32
“At the same time, it has been proved or otherwise assumed that fairness in exchange can be achieved without having a Trusted Third Party (TTP) [22]–[24].”
BackgroundBlockchain-Empowered Federated Learning: Benefits, Challenges, and Solutions
Cai, Z., Chen, J., Fan, Y., Zheng, Z., & Li, K. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3541560 · Cited by 40
“Each full node in a blockchain network maintains a local copy of the ledger, which ensures that malicious nodes are unable to dictate terms to other nodes unless they comply with the consensus algorithm.”
Bandara, E., Liang, X., Shetty, S. S., Mukkamala, R., Rahman, A., & Ng, W. K. (2022) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon55815.2022.9918536 · Cited by 36
“Any system that depends on a single entity for coordination or other key role is known to be vulnerable to attacks and privacy breaches [4], [5].”
BackgroundQualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond
Pathak, V., Pandya, R. J., Bhatia, V., & López, O. L. A. (2023) · IEEE Access · doi:10.1109/access.2023.3319083 · Cited by 34
Privacy preserving and secure robust federated learning: A survey
Han, Q., Lu, S., Wang, W., Qu, H., Li, J., & Gao, Y. (2024) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.8084 · Cited by 29
Reputation-Aware Multi-Agent DRL for Secure Hierarchical Federated Learning in IoT
Al-Maslamani, N. M., Abdallah, M. M., & Çiftler, B. S. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3280359 · Cited by 30
“The authors in [8] and [24] propose reputation and blockchain-based solutions to evaluate local model updates.”
BackgroundLu, Y., Qi, Y., Qi, S., Li, Y., Song, H., & Liu, Y. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3008315 · Cited by 32
“In addition, another work named DeepChain [51] provided a value-driven incentive mechanism based on Blockchain to force the participants to behave correctly.”
Method
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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