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Privacy-preserving blockchain-based federated learning for traffic flow prediction
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“Reference [23] added noise on the raw data by local differential privacy, but it reduced the model accuracy severely.”
From A Blockchain‐Based Federated Learning Method for Smart Healthcare (2021) · cites this paper as [23]
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“Qi et al. [53] combined blockchains with FL and introduced a noise-adding mechanism to address security problems with central servers in traffic flow forecasting.”
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Wang, Z., Hu, Q., Li, R., Xu, M., & Xiong, Z. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3253604 · Cited by 141
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BackgroundA 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
“Reference [23] added noise on the raw data by local differential privacy, but it reduced the model accuracy severely.”
BackgroundPrivacy-Aware Traffic Flow Prediction Based on Multi-Party Sensor Data with Zero Trust in Smart City
Wang, F., Li, G., Wang, Y., Rafique, W., Khosravi, M. R., Liu, G., Liu, Y., & Qi, L. (2022) · ACM Transactions on Internet Technology · doi:10.1145/3511904 · Cited by 119
“[35] propose a joint learning framework based on alliance blockchain.”
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“…BPDS[64] 2018 Medical Yes Permissioned CP-ABE Yes Fullmedicalrecordsarestoredinthecloud,andtheindexisstoredintheblockchain RaidenNetwork[42] 2018 Ethereum Yes Permissionless Statechannel No Off-chaintransactionsthroughsmartcontracts Blot[40] 2017 ZeroCash Yes Permissionless”
FusionFedBlock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0
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Decentralized federated learning for extended sensing in 6G connected vehicles
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“The miners perform a model quality evaluation scheme to evaluate quality of the local model updates [31], [32].”
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Risk prediction and credibility detection of network public opinion using blockchain technology
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Federated Learning and Its Role in the Privacy Preservation of IoT Devices
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“A single federated and enhanced global model is then supplied to the devices because these locally trained models are combined (i.e., weighted on average) [2].”
MethodIoT Malware Analysis Using Federated Learning: A Comprehensive Survey
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A survey on federated learning in data mining
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“The literature [15] uses LDP techniques to add noise to the raw training parameters directly to protect privacy, but it causes a large loss of model accuracy.”
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“Qi et al. [29] used blockchain to replace the central server in federated learning.”
BackgroundBlockchain-Enabled Asynchronous Federated Learning in Edge Computing
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“Some researchers also discussed the application of Blockchain-Enabled FL into vehicular networks [30,31].”
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MethodEdge Device Identification Based on Federated Learning and Network Traffic Feature Engineering
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“For instance, Qi et al. [32] proposed a consortium blockchain-based federated learning framework to update the model from distributed vehicles and then store on the blockchain, the experimental results show that it can effectively prevent data poisoning attacks and improve privacy protection.”
BackgroundA Review of the Key Technology in a Blockchain Building Decentralized Trust Platform
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Exploring privacy mechanisms and metrics in federated learning
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Definition of a novel federated learning approach to reduce communication costs
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Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing
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A Hierarchical Incentive Design Toward Motivating Participation in Coded Federated Learning
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Applying Federated Learning in Software-Defined Networks: A Survey
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“Qi et al. [117] introduced blockchain-based FL in IoVs, where workers voted a miner to supervise the aggregation in each iteration.”
BackgroundBlockchain-Based Decentralized Model Aggregation for Cross-Silo Federated Learning in Industry 4.0
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“Unlike [4] and [6], an HE-based solution is employed for secure model aggregation without exposing the local model parameters of the committee nodes.”
BackgroundMethodInfluentialFed-NTP: A Federated Learning Algorithm for Network Traffic Prediction in VANET
Sepasgozar, S. S., & Pierre, S. (2022) · IEEE Access · doi:10.1109/access.2022.3221970 · Cited by 48
“…the aim of traffic estimation were proposed in [11], a selective model aggregation based on FL for classification problem was proposed in [12], Privacy-preserving blockchain-based FL was proposed in [13] for predicting traffic flow and other hybrid methods based on FL were proposed in [14] and [15].”
MethodUAV-based MEC-assisted automated traffic management scheme using blockchain
Masuduzzaman, M., Islam, A., Sadia, K., & Shin, S. Y. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.04.018 · Cited by 47
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“Qi et al. [25] developed existing traffic flow prediction methods based on centralized machine learning, which require gathering raw data for model training, and involve serious privacy exposure risks.”
BackgroundInfluentialVerifiable Federated Learning With Privacy-Preserving Data Aggregation for Consumer Electronics
Xie, H., Wang, Y., Ding, Y., Yang, C., Zheng, H., & Qin, B. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3323206 · Cited by 46
“Due to the diverse requirements of federated learning in different scenarios, various solutions have been proposed to address specific challenges within federated learning, such as industrial IoT [8], vehicular networks [9], drones [10], and recommendation systems [11].”
BackgroundNg, J. S., Lim, W. Y. B., Xiong, Z., Cao, X., Jin, J., Niyato, D. T., Leung, C., & Miao, C. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3139039 · Cited by 45
“…remains the predominant approach for global model aggregation in FL, node failures, communication bottlenecks, and the single point of failure due to the computation and communication inefficiencies have proven to be key bottlenecks that impede the effective and scalable implementation of FL [10].”
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Fuzzy Consensus With Federated Learning Method in Medical Systems
Poap, D. (2021) · IEEE Access · doi:10.1109/access.2021.3125799 · Cited by 42
“Another issue which is improved in it is the privacy of models which can be improved and make the data, and models much safer is blockchain [15], [16].”
BackgroundOnline Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
Liu, Q., Sun, S., Liu, M., Wang, Y., & Gao, B. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3429533 · Cited by 52
“A great variety of methods have been proposed to increase the accuracy of traffic flow forecasting.”
MethodBFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction
Meese, C., Chen, H., Asif, S. A., Li, W., Shen, C.-C., & Nejad, M. (2022) · 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid) · doi:10.1109/ccgrid54584.2022.00041 · Cited by 41
“• Unlike existing works [5] [17] [18], we design a real-time FL process where the data shards for each participant (e.g., RSU) are distinct and private.”
A Survey on Blockchain-Based Federated Learning
Wu, L., Ruan, W., Hu, J., & He, Y. (2023) · Future Internet · doi:10.3390/fi15120400 · Cited by 48
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Artificial Intelligence techniques to mitigate cyber-attacks within vehicular networks: Survey
Haddaji, A., Ayed, S., & Fourati, L. C. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108460 · Cited by 43
A Survey of the Social Internet of Vehicles: Secure Data Issues, Solutions, and Federated Learning
Xing, L., Zhao, P., Gao, J., Wu, H., & Ma, H. (2022) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2022.3190036 · Cited by 39
“…[86] High security High efficiency High computational communication Autonomous-based blockchain–FL mechanism [88] FL-based consortium blockchain [89] DL FL-based deep learning [92] High robustness Generalization ability Highly dynamic data Unreliable Federated deep reinforcement learning [95]…”
BackgroundA Survey on Blockchain-Based Federated Learning and Data Privacy
Chhetri, B., Gopali, S., Olapojoye, R., Sani, S. D., & Namin, A. S. (2023) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac57700.2023.00199 · Cited by 42
“[27] BFL- Traffic Flow Prediction (TFP) Slower convergence rate due to its decentralized learning paradigm and model aggregation step, as opposed to the SAE model’s centralized learning.”
BackgroundResultWijesekara, P. A. D. S. N., & Gunawardena, S. (2023) · Network · doi:10.3390/network3030017 · Cited by 48
Asynchronous Federated Learning System Based on Permissioned Blockchains
Wang, R., & Tsai, W. (2022) · Sensors · doi:10.3390/s22041672 · Cited by 37
“[33] applied local differential privacy techniques to blockchain federation learning to protect data privacy in the industrial Internet and smart transportation domains by adding noisy perturbations to the raw data.”
BackgroundBlockchain-Empowered AI for 6G-Enabled Internet of Vehicles
Ayaz, F., Sheng, Z., Tian, D., Nekovee, M., & Saeed, N. H. (2022) · Electronics · doi:10.3390/electronics11203339 · Cited by 36
“, inaccurate traffic flow estimation in [15], inaccurate block addition in [16], and the percentage of nodes not receiving the message in our solution.”
BackgroundMethodInfluentialExploiting 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
“For another example, the joint learning scheme for traffic prediction based on the privacy protection blockchain [90] can effectively ensure the privacy of the model.”
BackgroundMethodInfluentialMultimodal Transport Demand Forecasting via Federated Learning
Li, C., & Liu, W. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3325936 · Cited by 37
“• FedGRU [47]: FedGRU is the combination of GRU and the basic Federated Learning framework, which is used in experiments to predict demands of multiple clients simultaneously.”
MethodAFLPC: An Asynchronous Federated Learning Privacy-Preserving Computing Model Applied to 5G-V2X
Huang, J., Xu, C., Ji, Z., Xiao, S., Liu, T., Ma, N., & Zhou, Q. (2022) · Security and Communication Networks · doi:10.1155/2022/9334943 · Cited by 36
“[14] use local differential privacy technology to add noise to the original training data to protect privacy, but it will cause a greater loss of model accuracy.”
BackgroundRetractedLightweight 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
Asqah, M. A., & Moulahi, T. (2023) · Future Internet · doi:10.3390/fi15060203 · Cited by 41
“Moreover, the work of [31] implemented federated learning into Blockchain to collaboratively train a traffic flow prediction (TFP) system of a neural network (NN) learning model.”
MethodFL-ODP: An Optimized Differential Privacy Enabled Privacy Preserving Federated Learning
Iqbal, M., Tariq, A., Adnan, M., Din, I. U., & Qayyum, T. (2023) · IEEE Access · doi:10.1109/access.2023.3325396 · Cited by 40
“Another approach involves a trustworthy aggregator managing the privacy exposure to ensure the model’s performance privacy [13].”
BackgroundBibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020
Luo, J., Hu, Y., & Bai, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3092192 · Cited by 37
“Federated learning (where data is kept on the owner’s device and updates to the model are summarized through security protocols) is the latest technology to support secure multi-party machine learning [86], has recently been introduced as an efficient solution for achieving privacy protection [87].”
BackgroundToward Crowdsourced Transportation Mode Identification: A Semisupervised Federated Learning Approach
Zhang, C., Zhu, Y., Markos, C., Yu, S., & Yu, J. J. Q. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3132056 · Cited by 34
“Successful applications can be seen in urban traffic forecasting [8], [9], rail traffic control [10], vehicular networking [11], and others.”
BackgroundMohammed, M. A., Lakhan, A. R., Abdulkareem, K. H., Ghani, M. K. A., Marhoon, H. A., Nedoma, J., & Martínek, R. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21639 · Cited by 34
Al-Huthaifi, R., Li, T., Al‐Huda, Z., Huang, W., Luo, Z., & Xie, P. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112029 · Cited by 37
Han, B., Li, B., Jurdak, R., Zhang, P., Zhang, H. Y., Feng, P., & Yuen, C. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3524632 · Cited by 33
“To address privacy concerns, Qi et al. [41] proposed a consortium blockchain-based FL framework with DP for secure traffic flow prediction.”
InfluentialBlockchain consensus mechanisms comparison in fog computing: A systematic review
Alzoubi, Y. I., & Mishra, A. (2024) · ICT Express · doi:10.1016/j.icte.2024.02.008 · Cited by 34
Simultaneous Fault Diagnosis and Size Estimation Using Multitask Federated Incremental Learning
Zhong, K., Ding, Z., Zhang, H., Chen, H., & Zio, E. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3402308 · Cited by 32
“Recently, Sheller et al. [8] proposed an intrusion detection method for an industrial network physical system based on FL. Qi et al. [9] adopted a novel FL scheme and applied it to the next-word prediction task of smartphone virtual keyboard.”
IoV security and privacy survey: issues, countermeasures, and challenges
Haddaji, A., Ayed, S., & Fourati, L. C. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06269-5 · Cited by 32
Shen, X., Chen, J., Zhu, S., & Yan, R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122302 · Cited by 32
Meese, C., Chen, H., Li, W., Lee, D. H., Guo, H., Shen, C., & Nejad, M. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3391053 · Cited by 30
“• To study online FL in ITS, contrary to existing works, [15], [22], [23], we design an online FL process that considers the location-specific and time-dependent datasets of each client.”
MethodA state-of-the-art on federated learning for vehicular communications
Drissi, M. (2023) · Vehicular Communications · doi:10.1016/j.vehcom.2023.100709 · Cited by 30
FedGCN: A Federated Graph Convolutional Network for Privacy-Preserving Traffic Prediction
Hu, N., Liang, W., Zhang, D., Xie, K., Li, K., & Zomaya, A. Y. (2024) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2024.3395350 · Cited by 30
Multi-factor embedding GNN-based traffic flow prediction considering intersection similarity
Zhong, R., Hu, B., Wang, F., Feng, Y., Li, Z., Song, X., Wang, Y., Lou, S., & Tan, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129193 · Cited by 30
An intelligent traffic prediction framework for 5G network using SDN and fusion learning
Selvi, K. T., & Thamilselvan, R. (2022) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-021-01280-6 · Cited by 29
“Federated learning [5] finds its application in many domains next word prediction, emoji prediction, prediction models [6] in health care to ensure the privacy of the patient data and many more.”
BackgroundMethodRobust ensemble method for short-term traffic flow prediction
Yan, H., Fu, L., Qi, Y., Yu, D., & Ye, Q. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.03.034 · Cited by 30
Self-citationQi, Y., Wu, J., Bashir, A. K., Lin, X., Yang, W., & Alshehri, M. D. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3215326 · Cited by 29
“In [30], a blockchain-based FL framework was proposed to tackle the urban traffic forecasting issue.”
Self-citationBFKD: Blockchain-Based Federated Knowledge Distillation for Aviation Internet of Things
Deng, W., Li, X., Xu, J., Li, W., Zhu, G., & Zhao, H. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3474710 · Cited by 28
“[28]introduceda blockchain-enabled decentralized FL mechanism that verifies clientmodelupdates,ensuringthatonlytrustednodesparticipate inaggregation.”
Self-citationLarge Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks
Zhang, Z., Sun, Y., Wang, Z., Nie, Y., Ma, X., Li, R., Sun, P., & Ban, X. J. (2025) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981251367699 · Cited by 72
“…another privacy-preserving blockchain-based framework for traffic flow prediction has been proposed, which stores model updates from distributed vehicles on the blockchain and leverages a differential privacy method with a noise-adding mechanism to enhance location privacy protection ( 128 ).”
Federated clustering for recognizing driving styles from private trajectories
Lü, L. T., Lin, Y., Wen, Y., Zhu, J., & Xiong, S. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105714 · Cited by 28
Match Maximization of Vehicle-to-Vehicle Energy Charging With Double-Sided Auction
Yassine, A., & Hossain, M. S. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3265870 · Cited by 28
“The system has the capability to track the EVs via a communication link (e.g., VANET, 5G, LoRaWAN, etc.) while assuring location privacy similar to the studies in [31], [32], and [33].”
BackgroundSelf-citation
Metadata
- 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
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 117
- Pages
- 328-337
- ISSN
- 0167-739X (ISSN-L)
- 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
- 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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