Privacy-preserving blockchain-based federated learning for traffic flow prediction
Integrity signals
Registry
Cites
Statistics
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?
Adding noise on raw data by local differential privacy severely reduces model accuracy.
“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]
Cites
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) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
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 · see citing works
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 2,199 · see citing works
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 · see citing works
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 · see citing works
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach
Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991401 · Cited by 712 · see citing works
Guo, J., Huang, W., & Williams, B. M. (2014) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2014.02.006 · Cited by 645 · see citing works
Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J., & Hossain, M. S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3011726 · Cited by 595 · see citing works
Self-citationEfficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence
Hao, M., Li, H., Luo, X., Xu, G., Yang, H., & Liu, S. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2945367 · Cited by 569 · see citing works
Certified Defenses for Data Poisoning Attacks
Steinhardt, J., Koh, P. W., & Liang, P. (2017) · arXiv · doi:10.48550/arxiv.1706.03691 · Cited by 479 · see citing works
Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing
Qu, Y., Gao, L., Luan, T. H., Xiang, Y., Yu, S., Li, B., & Zheng, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2977383 · Cited by 451 · see citing works
Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems
Duan, M., Liu, D., Chen, X., Liu, R., Tan, Y., & Liang, L. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3009406 · Cited by 434 · see citing works
Mitigating Sybils in Federated Learning Poisoning
Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2018) · arXiv · doi:10.48550/arxiv.1808.04866 · Cited by 385 · see citing works
Accelerating Federated Learning via Momentum Gradient Descent
Liu, W., Chen, L., Chen, Y., & Zhang, W. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2975189 · Cited by 374 · see citing works
Automatic fruit classification using deep learning for industrial applications
Hossain, M. S., Al-Hammadi, M., & Muhammad, G. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2875149 · Cited by 347 · see citing works
Self-citationA generic framework for privacy preserving deep learning
Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., & Passerat-Palmbach, J. (2018) · arXiv · doi:10.48550/arxiv.1811.04017 · Cited by 335 · see citing works
Vinayakumar, R., Alazab, M., Srinivasan, S., Pham, Q., Soman, K. P., & Simran, K. (2020) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2020.2971952 · Cited by 323 · see citing works
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 · see citing works
Towards 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 · see citing works
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
Zhang, L., Ding, G., Wu, Q., Zou, Y., Han, Z., & Wang, J. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2422735 · Cited by 249 · see citing works
A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems
Arachchige, P. C. M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., & Atiquzzaman, M. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2974555 · Cited by 236 · see citing works
2015 IEEE Trustcom/BigDataSE/ISPA
(2015) · doi:10.1109/trustcom/bigdatase/i36604.2015 · Cited by 213 · see citing works
A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion
Cao, Z., Siwei, J., Zhang, J., & Guo, H. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2613997 · Cited by 195 · see citing works
An AutoEncoder and LSTM-Based Traffic Flow Prediction Method
Wei, W., Wu, H., & Ma, H. (2019) · Sensors · doi:10.3390/s19132946 · Cited by 193 · see citing works
Groupchain: Towards a Scalable Public Blockchain in Fog Computing of IoT Services Computing
Lei, K., Du, M., Huang, J., & Jin, T. (2020) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2949801 · Cited by 153 · see citing works
Cloud-assisted secure video transmission and sharing framework for smart cities
Hossain, M. S., Muhammad, G., Abdul, W., Song, B., & Gupta, B. B. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.03.029 · Cited by 150 · see citing works
Self-citationEdge Intelligence in the Cognitive Internet of Things: Improving Sensitivity and Interactivity
Zhang⋆, Y., Ma, X., Zhang, J., Hossain, M. S., Muhammad, G., & Amin, S. U. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800344 · Cited by 149 · see citing works
Self-citationBig Data for Cybersecurity: Vulnerability Disclosure Trends and Dependencies
Tang, M., Alazab, M., & Luo, Y. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2723570 · Cited by 146 · see citing works
Zhao, F., Zeng, G., & Lu, K. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2952605 · Cited by 146 · see citing works
Secure Quantum Steganography Protocol for Fog Cloud Internet of Things
El‐Latif, A. A. A., Abd-El-Atty, B., Hossain, M. S., Elmougy, S., & Ghoneim, A. (2018) · IEEE Access · doi:10.1109/access.2018.2799879 · Cited by 126 · see citing works
Self-citationDeep Federated Q-Learning-Based Network Slicing for Industrial IoT
Messaoud, S., Bradai, A., Ahmed, O. B., Quang, P. T. A., Atri, M., & Hossain, M. S. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3032165 · Cited by 117 · see citing works
Self-citationHighly efficient federated learning with strong privacy preservation in cloud computing
Chen, F., Guo, Y., Wang, N., & Ju, A. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101889 · Cited by 112 · see citing works
Securing parked vehicle assisted fog computing with blockchain and optimal smart contract design
Huang, X., Ye, D., Yu, R., & Shu, L. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003039 · Cited by 108 · see citing works
Yang, X., Zhang, T., Xu, C., Yan, S., Hossain, M. S., & Ghoneim, A. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2582379 · Cited by 98 · see citing works
Self-citationFinding the Shortest Path in Stochastic Vehicle Routing: A Cardinality Minimization Approach
Cao, Z., Guo, H., Zhang, J., Niyato, D. T., & Fastenrath, U. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498160 · Cited by 94 · see citing works
Machine Learning Based Botnet Identification Traffic
Azab, A., Alazab, M., & Aiash, M. (2016) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications / BigDataSE / ISPA (Trustcom/BigDataSE/ISPA) · doi:10.1109/trustcom.2016.0275 · Cited by 94 · see citing works
PPGAN: Privacy-Preserving Generative Adversarial Network
Liu, Y., Peng, J., Yu, J. J. Q., & Wu, Y. (2019) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads47876.2019.00150 · Cited by 90 · see citing works
Secure Enforcement in Cognitive Internet of Vehicles
Qian, Y., Chen, M., Chen, J., Hossain, M. S., & Alamri, A. M. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2800035 · Cited by 79 · see citing works
Self-citationFast authentication in wireless sensor networks
Benzaïd, C., Lounis, K., Al-Nemrat, A., Badache, N., & Alazab, M. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.07.006 · Cited by 74 · see citing works
Towards context-sensitive collaborative media recommender system
Alhamid, M. F., Rawashdeh, M., Osman, H. A., Hossain, M. S., & Saddik, A. E. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2236-3 · Cited by 71 · see citing works
Self-citationGreen Video Transmission in the Mobile Cloud Networks
Lin, K., Song, J., Luo, J., Ji, W., Hossain, M. S., & Ghoneim, A. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2539618 · Cited by 68 · see citing works
Self-citationCrowdSFL: 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 68 · see citing works
Blockchain-Based Privacy-Aware Content Caching in Cognitive Internet of Vehicles
Qian, Y., Jiang, Y., Hu, L., Hossain, M. S., Alrashoud, M. R., & Al-Hammadi, M. H. (2020) · IEEE Network · doi:10.1109/mnet.001.1900161 · Cited by 64 · see citing works
Self-citationKang, J., Xiong, Z., Jiang, C., Liu, Y., Guo, S., Zhang, Y., Niyato, D. T., Leung, C., & Miao, C. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-9213-3_12 · Cited by 56 · see citing works
Yao, R., Zhang, W., & Zhang, L. (2020) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.0000388 · Cited by 47 · see citing works
A Deep-Tree-Model-Based Radio Resource Distribution for 5G Networks
Hossain, M. S., & Muhammad, G. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900286 · Cited by 37 · see citing works
Self-citationDetecting malicious behaviour using supervised learning algorithms of the function calls
Alazab, M., & Venkatraman, S. (2013) · International Journal of Electronic Security and Digital Forensics · doi:10.1504/ijesdf.2013.055047 · Cited by 25 · see citing works
GACNet: A Generative Adversarial Capsule Network for Regional Epitaxial Traffic Flow Prediction
Li, J., Hao, L., Cui, G., Kang, Y., Yang, H., & Zhou, Y. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.09903 · Cited by 14 · see citing works
Kang, J., Xiong, Z., Jiang, C., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.04743 · Cited by 11 · see citing works
Zhao, S., Zhao, Q., Bai, Y., & Li, S. (2019) · Electronics · doi:10.3390/electronics8091006 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A survey on federated learning: challenges and applications
Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., & Zhang, W. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01647-y · Cited by 904 · see citing works
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342 · see citing works
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 338 · see citing works
“[120] introduced a blockchain-enabled FL framework for securing the control of urban traffic flow.”
BackgroundShaygan, M., Meese, C., Li, W., Zhao, X., & Nejad, M. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2022.103921 · Cited by 292 · see citing works
Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges
Campos, E. M., Saura, P. F., González-Vidal, A., Hernández-Ramos, J. L., Bernabé, J. B., Baldini, G., & Skármeta, A. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108661 · Cited by 272 · see citing works
Zhang, C., Zhang, S., Yu, J. J. Q., & Yu, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3055283 · Cited by 194 · see citing works
“Qi et al. [26] introduced blockchain technique into FL for privacy-preserving traffic flow prediction.”
MethodYuan, X., Chen, J., Yang, J., Zhang, N., Yang, T., Han, T., & Taherkordi, A. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3157056 · Cited by 178 · see citing works
“8) FedGRU [31]: A federated deep learning approach namely FedGRU is based on GRU.”
BackgroundMethodFederated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Zhang, S., Li, J., Shi, L. X., Ding, M., Nguyen, D. C., Tan, W., Weng, J., & Han, Z. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3324962 · Cited by 169 · see citing works
Applications of Federated Learning; Taxonomy, Challenges, and Research Trends
Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157 · see citing works
Federated learning in smart cities: Privacy and security survey
Al-Huthaifi, R., Li, T., Huang, W. X., Gu, J., & Li, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.033 · Cited by 160 · see citing works
Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning
Xia, M., Jin, D., & Chen, J. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3179391 · Cited by 142 · see citing works
“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.”
BackgroundIncentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning
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 · see citing works
“S INCE its emergence in 2016, federated learning (FL) has been greatly developed and widely applied in many fields, such as Internet of Things [1]–[3], smart transportation [4], [5] and healthcare [6]–[8].”
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 · see citing works
“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 · see citing works
“[35] propose a joint learning framework based on alliance blockchain.”
BackgroundLiang, W., Liu, Y., Yang, C., Xie, S., Li, K., & Susilo, W. (2024) · ACM Computing Surveys · doi:10.1145/3676164 · Cited by 127 · see citing works
“…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
Singh, S. K., Yang, L. T., & Park, J. H. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.027 · Cited by 115 · see citing works
Decentralized federated learning for extended sensing in 6G connected vehicles
Barbieri, L., Savazzi, S., Brambilla, M., & Nicoli, M. (2021) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100396 · Cited by 109 · see citing works
Kang, J., Li, X., Nie, J., Liu, Y., Xu, M., Xiong, Z., Niyato, D. T., & Qiang, Y. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3178970 · Cited by 102 · see citing works
“The miners perform a model quality evaluation scheme to evaluate quality of the local model updates [31], [32].”
BackgroundMethodSelf-citationZhou, X., Huang, W., Liang, W. Y., Yan, Z., Ma, J., Pan, Y., & Wang, K. I. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120217 · Cited by 104 · see citing works
Blockchain-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 97 · see citing works
“In other work, authors [46] propose an FL learning structure based on blockchain which allows using secure, trusty FL as a method to solve the problem of centralized FL which has many security problems with chances of privacy leaks in the process of gathering unprocessed data in the model.”
BackgroundBatool, H., Anjum, A., Khan, A., Izzo, S., Mazzocca, C., & Jeon, G. (2024) · Information Sciences · doi:10.1016/j.ins.2023.119717 · Cited by 89 · see citing works
Risk prediction and credibility detection of network public opinion using blockchain technology
Wang, Z., Zhang, S., Zhao, Y., Chen, C., & Dong, X. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122177 · Cited by 91 · see citing works
A Survey on Federated Learning in Intelligent Transportation Systems
Zhang, R., Mao, J., Wang, H., Li, B., Cheng, X., & Yang, L. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3446319 · Cited by 99 · see citing works
“In this framework, the central server is replaced by a set of trusted consensus nodes to mitigate the risk of single point of failure.”
A comprehensive review on blockchains for Internet of Vehicles: Challenges and directions
Hildebrand, B., Baza, M., Salman, T., Tabassum, S., Konatham, B., Amsaad, F., & Razaque, A. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100547 · Cited by 88 · see citing works
Privacy-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 104 · see citing works
Mohammadi, S., Balador, A., Sinaei, S., & Flammini, F. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104918 · Cited by 88 · see citing works
Federated Learning and Its Role in the Privacy Preservation of IoT Devices
Alam, T., & Gupta, R. (2022) · Future Internet · doi:10.3390/fi14090246 · Cited by 84 · see citing works
“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
Venkatasubramanian, M., Lashkari, A. H., & Hakak, S. (2023) · IEEE Access · doi:10.1109/access.2023.3235389 · Cited by 80 · see citing works
“In the paper [104], the authors use blockchain technology, miners, and ledgers to regularly verify the local model updates.”
Rahman, M. A., Hossain, M. S., Showail, A. J., Alrajeh, N. A., & Alhamid, M. F. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103083 · Cited by 78 · see citing works
Self-citationFederated Learning-Based Collaborative Authentication Protocol for Shared Data in Social IoV
Zhao, P., Huang, Y., Gao, J., Xing, L., Wu, H., & Ma, H. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3153338 · Cited by 74 · see citing works
Moulahi, T., Jabbar, R., Alabdulatif, A., Abbas, S., Khediri, S. E., Zidi, S., & Rizwan, M. (2022) · Expert Systems · doi:10.1111/exsy.13103 · Cited by 74 · see citing works
“To resolve this issue, ‘ federated learning ’ (FL) (Qi et al., 2021) was developed by Google in 2016 for establishing data models distributive.”
Nazir, A., He, J., Zhu, N., Anwar, M. S., & Pathan, M. S. (2024) · Cluster Computing · doi:10.1007/s10586-024-04436-0 · Cited by 74 · see citing works
A survey on federated learning in data mining
Yu, B., Mao, W., Lv, Y., Zhang, C. B. C., & Xie, Y. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1443 · Cited by 78 · see citing works
“As shown in Y. Qi et al. (2021), federated learning is leveraged to protect the privacy of vehicle data in the task of traffic flow prediction.”
BackgroundImran, M. M. H., Jamaludin, S., & Ayob, A. F. M. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.116796 · Cited by 75 · see citing works
Li, X., Zhao, H., Xu, J., Zhu, G., & Deng, W. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3479149 · Cited by 71 · see citing works
Self-citationAhmadi, M., Taghavirashidizadeh, A., Javaheri, D., Masoumian, A., Ghoushchi, S. J., & Pourasad, Y. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.08.019 · Cited by 70 · see citing works
A Blockchain-Based Federated Learning Scheme for Data Sharing in Industrial Internet of Things
Xu, G., Zhou, Z., Dong, J., Zhang, L., & Song, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3298196 · Cited by 68 · see citing works
“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.”
BackgroundMethodSun, Y., Ochiai, H., & Esaki, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3133819 · Cited by 73 · see citing works
Zhang, C., Xu, Y., Elahi, H., Zhang, D., Tan, Y., Chen, J., & Zhang, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3171926 · Cited by 67 · see citing works
“Qi et al. [29] used blockchain to replace the central server in federated learning.”
BackgroundBlockchain-Enabled Asynchronous Federated Learning in Edge Computing
Liu, Y., Qu, Y., Xu, C., Hao, Z., & Gu, B. (2021) · Sensors · doi:10.3390/s21103335 · Cited by 65 · see citing works
“Some researchers also discussed the application of Blockchain-Enabled FL into vehicular networks [30,31].”
BackgroundDecentralized federated learning based on blockchain: concepts, framework, and challenges
Zhang, H., Jiang, S., & Xuan, S. (2024) · Computer Communications · doi:10.1016/j.comcom.2023.12.042 · Cited by 65 · see citing works
Ahmed, A. A., & Alabi, O. O. (2024) · IEEE Access · doi:10.1109/access.2024.3429205 · Cited by 59 · see citing works
“To address these data privacy concerns, several FL frameworks were proposed in the literature [13], [16], [18], [19], [20], [21], [22] where only trained models are shared with the participating entities instead of the raw datasets, thus preserving data confidentiality and utility while upholding…”
MethodEdge Device Identification Based on Federated Learning and Network Traffic Feature Engineering
He, Z., Yin, J., Wang, Y., Gui, G., Adebisi, B., Ohtsuki, T. O., Gacanin, H., & Sari, H. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2021.3101239 · Cited by 57 · see citing works
“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
Deng, W., Huang, T., & Wang, H. (2022) · Mathematics · doi:10.3390/math11010101 · Cited by 61 · see citing works
Exploring privacy mechanisms and metrics in federated learning
Shenoy, D., Bhat, R., & Prakash, K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11170-5 · Cited by 56 · see citing works
Definition of a novel federated learning approach to reduce communication costs
Paragliola, G., & Coronato, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116109 · Cited by 54 · see citing works
Blockchain 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 54 · see citing works
A Hierarchical Incentive Design Toward Motivating Participation in Coded Federated Learning
Ng, J. S., Lim, W. Y. B., Xiong, Z., Cao, X., Niyato, D. T., Leung, C., & Kim, D. I. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126057 · Cited by 54 · see citing works
Salim, S., Turnbull, B., & Moustafa, N. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3134463 · Cited by 54 · see citing works
“2) FL Task Assignment Phase: In a typical blockchain-based FL configuration, the entities’ responsibilities toward the FL task and the blockchain are assigned each round either randomly (such as with [36]) or based on the amount of reputation that they hold in the blockchain (as per [37]).”
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 · see citing works
Yuan, X., Chen, J., Zhang, N., Fang, X., & Liu, D. (2021) · China Communications · doi:10.23919/jcc.2021.07.010 · Cited by 49 · see citing works
Dewangan, N. K., Chandrakar, P., Kumari, S., & Rodrigues, J. J. P. C. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13915-8 · Cited by 48 · see citing works
Federated learning for big data: A survey on opportunities, applications, and future directions
Gadekallu, T. R., Pham, Q., Huynh‐The, T., Feng, H., Fang, K., Pandya, S., Liyanage, M., Wang, W. W., & Nguyen, T. T. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113614 · Cited by 81 · see citing works
Whig, P., Sharma, R., Yathiraju, N., Jain, A., & Sharma, S. (2024) · doi:10.1002/9781394219230.ch16 · Cited by 48 · see citing works
Applying Federated Learning in Software-Defined Networks: A Survey
Ma, X., Liao, L., Li, Z., Lai, X.-R., & Zhang, M. (2022) · Symmetry · doi:10.3390/sym14020195 · Cited by 39 · see citing works
“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
Ranathunga, T., McGibney, A., Rea, S., & Bharti, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3218704 · Cited by 47 · see citing works
“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 · see citing works
“…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 · see citing works
Blockchain-empowered secure federated learning system: Architecture and applications
Yu, F., Lin, H. D., Wang, X., Yassine, A., & Hossain, M. S. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.008 · Cited by 51 · see citing works
Self-citationMoore, E., Imteaj, A., Rezapour, S., & Amini, M. H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3313055 · Cited by 52 · see citing works
“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 · see citing works
“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 · see citing works
“…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].”
BackgroundAl-Huthaifi, R., Li, T., Al‐Huda, Z., & Li, C. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120482 · Cited by 44 · see citing works
Fuzzy Consensus With Federated Learning Method in Medical Systems
Poap, D. (2021) · IEEE Access · doi:10.1109/access.2021.3125799 · Cited by 42 · see citing works
“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 42 · see citing works
“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 · see citing works
“• 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 · see citing works
Chen, S., Liu, Y., Zhang, Q., Shao, Z., & Wang, Z. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400898 · Cited by 41 · see citing works
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 · see citing works
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 · see citing works
“…[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 · see citing works
“[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 44 · see citing works
Asynchronous Federated Learning System Based on Permissioned Blockchains
Wang, R., & Tsai, W. (2022) · Sensors · doi:10.3390/s22041672 · Cited by 37 · see citing works
“[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 · see citing works
“, 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 40 · see citing works
“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 36 · see citing works
“• 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 · see citing works
“[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 · see citing works
Asqah, M. A., & Moulahi, T. (2023) · Future Internet · doi:10.3390/fi15060203 · Cited by 35 · see citing works
“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 36 · see citing works
“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 35 · see citing works
“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 · see citing works
“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 · see citing works
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 33 · see citing works
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 · see citing works
“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 33 · see citing works
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 · see citing works
“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 · see citing works
Shen, X., Chen, J., Zhu, S., & Yan, R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122302 · Cited by 30 · see citing works
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 · see citing works
“• 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 · see citing works
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 29 · see citing works
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 29 · see citing works
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 · see citing works
“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. F., Yu, D., & Ye, Q. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.03.034 · Cited by 29 · see citing works
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 · see citing works
“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 · see citing works
“[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 28 · see citing works
“…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 · see citing works
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 · see citing works
“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
- 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 › Artificial Intelligence › Cryptography and Data Security
- Keywords
- traffic flow prediction, federated learning, blockchain, consortium blockchain, differential privacy, data poisoning, privacy preservation, intelligent transportation systems, noise addition
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
