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Privacy-preserving blockchain-based federated learning for traffic flow prediction

Qi, Yuanhang, Hossain, M. Shamim, Nie, Jiangtian, Li, Xuandi·2021·Future Generation Computer Systems

DOI 10.1016/j.future.2020.12.003

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Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000
Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.03.014 · Cited by 2,259
Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory
Kang, 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
Support vector machines for wind speed prediction
Mohandes, M. A., Halawani, T. O., Rehman, S., & Hussain, A. A. (2004) · Renewable Energy · doi:10.1016/j.renene.2003.11.009 · Cited by 756
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach
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Alferaidi, A., Yadav, K., Alharbi, Y. O., Alreshidi, E. J., Alreshidi, A., Aboshosha, B. W., Sharma, R., Alkhayyat, A. H., & Aray, D. G. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3412199 · Cited by 28
“As a result, all IoT devices will have a signiﬁcant decline in accuracy as a result of a crash or malicious modi-ﬁcation of the global model by the central server (e.g., Edge server) [18], [19], [20], [21], [22], [23], [24].”
B 2 SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction
Guo, H., Meese, C., Li, W., Shen, C.-C., & Nejad, M. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3318990 · Cited by 26
“Qi et al. [22] proposed the privacy-preserving blockchain-based federated learning for trafﬁc ﬂow prediction task, they also applied a differential privacy method with a noise-adding mechanism to prevent data poisoning attacks.”
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Zhu, C., Xiao, Z., & Qin, T. (2024) · Sensors · doi:10.3390/s24051364 · Cited by 26
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“To address current issues of traditional FL, such as single point failure, lack of incentive mechanism, and data falsiﬁcation, blockchain-enabled federated learning [8], [9], [17], [18], [19], [20], [40] is proposed and attract massive attention from both academia and industry.”
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Li, X., Zeng, J., Chen, C., Chi, H.-L., & Shen, Q. (2022) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12891 · Cited by 26
“Qi et al. (2021) also established an FL framework with a consortium blockchain by involving a noise-adding mechanism for differential privacy methods to improve the privacy preservation of traffic flow prediction.”
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“Whilst workarounds to some of the difﬁculties have been presented Xu et al. (2020), Qi et al. (2021), there is no extant work in the blockchain-based FL that addresses all three challenges at the same time using blockchain-based solutions.”
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“Similarly, Qi et al. [33] introduced a decentralized framework that established a dependable and secure foundation for federated learning, eliminating the necessity for a centralized model coordinator.”
Background
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Tan, G., Yuan, H., Hu, H., Zhou, S., & Zhang, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3385913 · Cited by 25
“In [18], an algorithm based on FL was proposed to predict trafﬁc patterns, while Liang et al. [19] presented a federated transfer reinforcement learning (RL) approach, utilized for the extraction of knowledge in real-time.”
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Zhang, S., Jiang, P., Li, X., Yin, C., & Wang, X. V. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102756 · Cited by 23
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“For blockchain-enabled FL with decentralized learning, the paper in [212] proposes a blockchain-based FL model for privacy-preserving traffic flow prediction, authors in [213] integrate blockchain and FL for privacy-preserving mobile crowdsensing, the paper in [214] presents a blockchain-enabled FL…”
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“Recently, in addition to the perspectives of mobile devices, the adoption of FL on top of the 6G-MEC infrastructure has triggered some recent studies on FL-based network trafﬁc prediction schemes [5], [15], [16], [17], [18], [19].”
Background
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Djenouri, Y., Belhadi, A., Djenouri, D., Srivastava, G., & Lin, J. C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3243542 · Cited by 21
“We optimize the algorithm created in [47] to accurately implement the above steps.”
Method
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“Reference [31] proposed a federated learning framework based on a consortium blockchain, where model updates from distributed vehicles are verified by miners to prevent unreliable model updates and then stored on the blockchain.”
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“One of the primary challenges is ensuring the quality and consistency of data collected from various IoT sensors [19].”
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“detection, while the shared knowledge from internal reasons has been neglected [4], [5].”
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“This processisrepeateduntilthevalidityoftheglobalaggregated model[4],[5].”
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Ayaz, F., Sheng, Z., Ho, I. W.-H., Tiany, D., & Ding, Z. (2022) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2022-spring54318.2022.9860421 · Cited by 18
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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
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“Qi et al. proposed block-chain-enabled FL-based scheme for traffic flow prediction [32].”
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“[57] Introduced blockchain-based FL in Internet of Vehicles (IoVs), allowing users to vote on a miner to control the aggregation at the end of each iteration.”
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Self-citation
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“Reference number Technique used Accuracy Precision Detection rate F-measure [12] DL 90 87 88 89 [23] DBTPPS 87 86 88 87 [24] ACO 85 86 87 86 [25] BFL 82 83 84 85 [25] MATWS 86 85 84 87 [27] BBTM 86 87 88 89 [21] DRNN 89 89 89 90 [22] HDRNN 90 90 91 92 [6] SDPN-SMO 92 89 92 93 [30] OWASP 93 92 93 94 Proposed GTBSS-HDNN…”
Background
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“To solve security challenges, the authors in [41] proposed a privacy-preserving blockchain-based federated learning for trafﬁc ﬂow prediction.”
Background
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Decentralized IoT data sharing: A blockchain-based federated learning approach with joint optimizations for efficiency and privacy
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An effective Federated Learning system for Industrial IoT data streaming
Wu, Y., Yang, H., Wang, X., Yu, H., Saddik, A. E., & Hossain, M. S. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.07.040 · Cited by 13
Self-citation
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Self-citation
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“In some work, Blockchain was used to reduce security risks [31], [32], [33], [34], [35], [36], [37], [38].”
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Li, K., Zhou, H., Tu, Z., Liu, F., & Zhang, H. (2022) · Security and Communication Networks · doi:10.1155/2022/4233238 · Cited by 14
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“Qi et al. [27] proposed a consortium blockchain-based FL framework for decentralized, reliable, and secure FL in trafﬁc ﬂow prediction without a centralized model coordinator.”
Background
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Chen, C., Hao, Y., Kumari, S., & Amoon, M. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3476969 · Cited by 12
“This section elucidates the Delegated Byzantine Fault Tolerance (DBFT) consensus algorithm [36] employed and delineates the meticulous procedure of aggregating local models.”
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“Our proposed federated learning model reduces the storage cost by sharing only the parameters of models rather than the original parameter data of each sensor node [41].”
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“Yuanhang et al. [22] intended to devise a ﬂow prediction model (FPM) usingblockchain-basedfederatedlearning.”
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Jia, Y., Xiong, L., Fan, Y., Liang, W., Xiong, N. N., & Xiao, F. (2024) · Connection Science · doi:10.1080/09540091.2023.2299103 · Cited by 12
“To cope the challenge, Wan et al. (2022b) proposed an FL privacy-preserving scheme based on blockchain for B5G-Driven edge computing, which protects the FL local model parameters privacy through the integration of Wasserstein Generative Adversarial Network and DP. Qi et al. (2021) use DP to protect vehicle location to…”
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“Ensuring traceability reduces data misuse risks signiﬁcantly, which is vital in sensitive ﬁelds like healthcare [16], ﬁnance [17], and government data management [18], [19].”
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Sen, A., Heng, S., & Tan, S. C. (2025) · IEEE Access · doi:10.1109/access.2025.3593953 · Cited by 14
“Lu et al. [76] and Qi et al. [84] applied blockchain techniques, combined with perturbation approaches, to ensure secure and decentralised learning in IoT and transportation systems.”
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A Decentralized ETC Architecture Based on Blockchain Technology
Huang, S., Yang, L., Yang, X., Li, X., & Gao, F. (2021) · Journal of Advanced Transportation · doi:10.1155/2021/8848697 · Cited by 10
“In addition, blockchain technology is widely used in traffic signal control mechanisms [9], traffic flow forecasts [10], and traffic information systems [11] in the transportation field.”
BackgroundSelf-citation
Healthcare Data Protection using Federated Learning Technology
Gobinath, B., & Shanmugam, T. K. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142903 · Cited by 10
“A data exchange and preservation method based on blockchain and FL was proposed in [27].”
Method
Analysis and evaluation of synchronous and asynchronous FLchain
Wilhelmi, F., Giupponi, L., & Dini, P. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109390 · Cited by 10
“FLchain has been embraced for 5G and beyond applications [36], including use cases such as data sharing in industrial IoT [37], enabling data-driven cognitive computing in Industry 4.0 [38], or enhancing trust in autonomous vehicles [39], [40] (e.g., mobility veriﬁcation, trafﬁc prediction).”
Influential
Hybrid fuzzy grammar dynamic graph diffusing attention network for traffic flow prediction
Zhang, D., Zhang, Z., Jiao, X., & Zhang, Y. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107725 · Cited by 9
SRFL: A Swarm-Reputation-Based Autonomic Federated Learning Framework for AIoT
Zhang, W., Du, M., Guo, X., & Xiong, N. N. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3509264 · Cited by 12
Blockchain-Enabled Human Resource Management for Enhancing Transparency, Trust, and Talent Mobility in the Digital Era
Madanchian, M., & Taherdoost, H. (2026) · Blockchains · doi:10.3390/blockchains4010002 · Cited by 9
“Qi et al. [87] introduce a consortium blockchain-based federated learning system with differential privacy for protecting model updates.”
Secure Blockchain-Enabled Internet of Vehicles Scheme with Privacy Protection
Zhang, J., Yang, X., Wang, Y., Lei, X., & Yang, Y. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.038029 · Cited by 9
“In 2021, Qi et al. proposed a consortium blockchain-based federated learning framework, which can protect data privacy in industrial Internet by applying a noise-adding mechanism for the blockchain-based federated learning framework [16].”
Defending Data Poisoning Attack via Trusted Platform Module and Blockchain Oracle
Huang, M., Cao, S., Li, X., Huang, K., & Zhang, X. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838252 · Cited by 10
“Qi et al. [9] propose a blockchain-based federated learning framework, in which the model updates are verified by miners to prevent unreliable model updates.”
BackgroundSelf-citation
An accurate traffic flow prediction using long-short term memory and gated recurrent unit networks
Sawah, M. S., Taie, S. A., Ibrahim, M. H., & Hussein, S. A. (2023) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v12i3.5080 · Cited by 11
A traffic flow prediction framework based on integrated federated learning and Recurrent Long short-term networks
Pulligilla, M. K., & Vanmathi, C. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01792-x · Cited by 9
An Robust Secure Blockchain-Based Hierarchical Asynchronous Federated Learning Scheme for Internet of Things
Chen, Y., Linglongyan, & Ai, D. (2024) · IEEE Access · doi:10.1109/access.2024.3493112 · Cited by 10
“Integrated blockchain, FL improves the security, reliability, and tractability with the decentralized storage and attack-proof consensus algorithm [3], [18], [19], [20], [21], [22], [23], [24].”
Privacy-Preserving Approach to Edge Federated Learning Based on Blockchain and Fully Homomorphic Encryption
Deng, Y., Guo, B., & Chen, S. (2025) · Electronics · doi:10.3390/electronics14020361 · Cited by 13
A novel geo-independent and privacy-preserved traffic speed prediction framework based on deep learning for intelligent transportation systems
Akın, M., Canbay, Y., & Sağıroğlu, Ş. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-06979-4 · Cited by 9
Quantum-Safe Federated Learning: Enhancing Data Privacy and Security
Kannan, E., J, C. M. B. M., Ravikumar, S., Alex David, S., Kannan, S., & Vijay, K. (2024) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs63125.2024.10895353 · Cited by 8
“The utilization of privacy-preserving blockchain methodologies in federated learning for traffic flow forecasting was examined [18].”
Review of Privacy Preservation with Blockchain Technology in the Context of Smart Cities
Mezquita, Y., González‐Briones, A., Vara, R. C., Wolf, P., la Prieta, F. D., & González, A. B. G. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-78901-5_7 · Cited by 8
A Review of Privacy-Preserving Federated Learning, Deep Learning, and Machine Learning IIoT and IoTs Solutions
Obarafor, V., Qi, M., & Zhang, L. (2023) · doi:10.1109/icsip57908.2023.10270935 · Cited by 11
“Researchers in [5] focused on a federated learning framework for IIoT (smart transportation) to overcome the shortcomings of an existing decentralized architectural based framework which is susceptible to single point of failure.”
BackgroundMethodInfluential
The Entanglement of Communication and Computing in Enabling Edge Intelligence
Li, J., & Mahmoodi, T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3391923 · Cited by 8
“For further investigations of blockchain-aided FL, we refer the readers to work in [30], [31], and [32].”
Method
Heterogeneous Privacy-Preserving Blockchain-Enabled Federated Learning for Social Fintech
Xiong, H., Xia, Y., Zhao, Y., Wahaballa, A., & Yeh, K. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3533634 · Cited by 10
“Researchers have also explored the application of BEFL in various domains, such as vehicular networks [33], [34], where the decentralized and secure nature of blockchain can offer signiﬁcant beneﬁts.”
[Retracted] Privacy Protection of Medical Service Data Based on Blockchain and Artificial Intelligence in the Era of Smart Medical Care
Bo, W., Pi, Y., & Chen, J. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/5295801 · Cited by 8
Retracted
Data privacy issue in Federated Learning Resolution using Block Chain
Rasheed, M. A., Uddin, S., Tanweer, H. A., Rasheed, M. A., Ahmed, M., & Murtaza, H. (2021) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v9i4.726 · Cited by 9
IRS-assisted UAV wireless powered communication network for sustainable federated learning
Li, R., Zhang, G., & Chen, Y. (2024) · Physical Communication · doi:10.1016/j.phycom.2024.102504 · Cited by 7
Federated Learning for Intelligent Transportation Systems: Use Cases, Open Challenges, and Opportunities
Chong, Y.-W., Yau, K. A., Ibrahim, N. F., Rahim, S. K. A., Keoh, S. L., & Basuki, A. (2024) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2024.3451479 · Cited by 10
Dynamic Optimization of Vehicle Production Planning in Transportation Networks Using Federated Reinforcement Learning
Chen, J., Zhu, X., Chakraborty, C., Guduri, M., Alharbi, A., Tolba, A. K. R., & Yu, K. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3522523 · Cited by 7
“However, label-only attacks can still overcome CSM. Magalhães et al. [27] proposed integrating blockchain and Differential Privacy (DP) [21] to ensure data security.”
PPSTSL: A Privacy-preserving Dynamic Spatio-temporal Graph Data Federated Split Learning for traffic forecasting
Feng, Y., & Qian, Q. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103129 · Cited by 8
Decentralised machine learning in healthcare and life sciences: Applying the technology acceptance model
Förster, K., & Strauß, T. (2025) · Telematics and Informatics Reports · doi:10.1016/j.teler.2025.100198 · Cited by 10
The Impact of Federated Learning on Urban Computing
Souza, J. R. F., Oliveira, S. Z. L. N., & Oliveira, H. M. N. D. S. (2024) · Journal of Internet Services and Applications · doi:10.5753/jisa.2024.4006 · Cited by 8
“…data to enhance route planning [Bian et al ., 2023; Singh, 2023; Zeng et al ., 2021; Wilbur et al ., 2020], improve public transit [ Zhao et al ., 2022; Xu et al ., 2022], and implement smart traffic management [ Liu et al ., 2020a; Qi et al ., 2021; Liu et al ., 2023; Agarwal et al ., 2023].”
Background
Blockchain-Enabled Federated Learning with Differential Privacy for Internet of Vehicles
Cui, C., Du, H., Jia, Z., He, Y., & Wang, L. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.055557 · Cited by 7
VFLChain: Blockchain-enabled Vertical Federated Learning for Edge Network Data Sharing
Cheng, Z., Pan, Y., Liu, Y., Wang, B., Deng, X., & Zhu, C. (2022) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus55513.2022.9987097 · Cited by 7
A Privacy Preservation Framework Using Integration of Blockchain and Federated Learning
Sameera, K. M., Rehiman, K. A. R., & Vinod, P. (2023) · SN Computer Science · doi:10.1007/s42979-023-02075-7 · Cited by 9
Blockchain-based fairness-enhanced federated learning scheme against label flipping attack
Jin, S., Li, Y., Chen, X., Li, R., & Shen, Z. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103580 · Cited by 7
AFL-SecNet: An adaptive federated learning framework for secure and privacy-preserving network traffic analysis
Naz, A., Ullah, I., Uzair, M., Khokhar, M. F., Sabir, A., & Khan, R. U. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02155-w · Cited by 6

Authors: Qi, Y., Hossain, M. S., Nie, J., & Li, X.
Affiliations: University of Electronic Science and Technology of China (CN)
King Saud University (SA)
Nanyang Technological University (SG)
Corresponding author: Hossain, M. Shamim
ORCID: Qi, Yuanhang
Hossain, M. Shamim
Nie, Jiangtian
Li, Xuandi
Venue: Future Generation Computer Systems
Publisher: Elsevier BV
Published: 2021-04
Volume: 117
Pages: 328-337
ISSN: 0167-739X (ISSN-L)
DOI: 10.1016/j.future.2020.12.003
OpenAlex: W3112885687
Cited by: 339
References: 55
Citations by year: 2026: 31 · 2025: 49 · 2024: 99 · 2023: 70 · 2022: 60 · 2021: 30
FWCI: 15.47
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Future Generation Computer Systems (publishedVersion)
DR-NTU (Nanyang Technological University) (submittedVersion)
Funders: King Saud University, Riyadh, Saudi Arabia
Key Project in Higher Education of Guangdong Province, China (2020ZDZX3030)
Young Innovation Talents Project in Higher Education of Guangdong Province, China (2018KQNCX333)
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: traffic flow prediction, federated learning, blockchain, consortium blockchain, differential privacy, data poisoning, privacy preservation, intelligent transportation systems, noise addition

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