Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach
Open accessAbstract
Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of data sets gathered by governments and organizations. However, these data sets may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning (FL) and propose an FL-based gated recurrent unit neural network algorithm (FedGRU) for traffic flow prediction (TFP). FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations. In the secure parameter aggregation mechanism, we adopt a federated averaging algorithm to reduce the communication overhead during the model parameter transmission process. Furthermore, we design a joint announcement protocol to improve the scalability of FedGRU. We also propose an ensemble clustering-based scheme for TFP by grouping the organizations into clusters before applying the FedGRU algorithm. Extensive case studies on a real-world data set demonstrate that FedGRU can produce predictions that are merely 0.76 km/h worse than the state of the art in terms of mean average error under the privacy preservation constraint, confirming that the proposed model develops accurate traffic predictions without compromising the data privacy.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In [85], an improved FedAvg that uses random subsampling of participants reduced communication costs by limiting the number of participants in each round.
“In [85], an improved FedAvg that uses random subsampling of participants has helped reduce communication costs by limiting the number of participants in each round.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [85]
Medicine resistance or therapy against various diseases, as well as survival rates or explanations, can be examined with strong privacy towards sensitive data.
“Medicine resistance or therapy against various diseases, as well as survival rates or explanations, can be examined with strong privacy towards sensitive data [165].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [165]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · 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) · 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
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322 · see citing works
Self-citationFederated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210 · see citing works
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023 · 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
Constrained K-means Clustering with Background Knowledge
Wagstaff, K. L., Cardie, C., Rogers, S., & Schrödl, S. (2001) · Cited by 2,469 · see citing works
Towards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334 · 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
Williams, B. M., & Hoel, L. A. (2003) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(2003)129:6(664 · Cited by 2,184 · see citing works
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Nishio, T., & Yonetani, R. (2019) · ICC 2019 - 2019 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2019.8761315 · Cited by 1,875 · see citing works
Introduction to multi-layer feed-forward neural networks
Svozil, D., Kvasnicka, V., & Pospichal, J. (1997) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(97)00061-0 · Cited by 1,600 · see citing works
Using LSTM and GRU neural network methods for traffic flow prediction
Fu, R., Zhang, Z., & Li, L. (2016) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac.2016.7804912 · Cited by 1,555 · see citing works
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988604 · Cited by 1,005 · 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
Self-citationGSIS: A Secure and Privacy-Preserving Protocol for Vehicular Communications
Lin, X., Sun, X., Ho, P., & Shen, X. (2007) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2007.906878 · Cited by 917 · see citing works
Combining kohonen maps with arima time series models to forecast traffic flow
van der Voort, M. C., Dougherty, M. S., & Watson, S. (1996) · Transportation Research Part C Emerging Technologies · doi:10.1016/s0968-090x(97)82903-8 · Cited by 851 · see citing works
Verifynet: Secure and verifiable federated learning
Xu, G., Li, H., Liu, S., Yang, K., & Lin, X. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2929409 · Cited by 839 · see citing works
Boosting and Differential Privacy
Dwork, C., Rothblum, G. N., & Vadhan, S. (2010) · 2010 IEEE 51st Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2010.12 · Cited by 784 · 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
Reliable Federated Learning for Mobile Networks
Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900119 · Cited by 617 · see citing works
Self-citationPrivacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017377 · Cited by 613 · see citing works
Self-citationKang, J., Xiong, Z., Niyato, D., Ye, D., Kim, D. I., & Zhao, J. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2894944 · Cited by 604 · see citing works
Self-citationLee, S., & Fambro, D. B. (1999) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1678-22 · Cited by 547 · see citing works
Nonparametric Regression and Short‐Term Freeway Traffic Forecasting
Davis, G. A., & Nihan, N. L. (1991) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(1991)117:2(178 · Cited by 531 · see citing works
Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network
Tian, Y., & Pan, L. (2015) · IEEE International Conference on Smart City/SocialCom/SustainCom · doi:10.1109/smartcity.2015.63 · Cited by 526 · see citing works
Fair Resource Allocation in Federated Learning
Li, T., Sanjabi, M., Beirami, A., & Smith, V. (2019) · arXiv · doi:10.48550/arxiv.1905.10497 · Cited by 475 · see citing works
Practical Secure Aggregation for Federated Learning on User-Held DataPreprint
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04482 · Cited by 468 · see citing works
Virtual trip lines for distributed privacy-preserving traffic monitoring
Hoh, B., Gruteser, M., Herring, R., Ban, J. X., Work, D. B., Herrera, J. C., Bayen, A. M., Annavaram, M., & Jacobson, Q. A. (2008) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/1378600.1378604 · Cited by 355 · see citing works
Online Vehicle Routing With Neural Combinatorial Optimization and Deep Reinforcement Learning
Yu, J. J. Q., Yu, W., & Gu, J. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2909109 · Cited by 341 · 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
The Security of Autonomous Driving: Threats, Defenses, and Future Directions
Ren, K., Wang, Q., Wang, C., Qin, Z., & Lin, X. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2948775 · Cited by 223 · see citing works
Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting
Zhang, C., Yu, J. J. Q., & Liu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2953888 · Cited by 189 · see citing works
Self-citationDynaCAS: Computational Experiments and Decision Support for ITS
Zhang, N., Wang, F.-Y., Zhu, F., Zhao, D., & Tang, S. (2008) · IEEE Intelligent Systems · doi:10.1109/mis.2008.101 · Cited by 134 · see citing works
Zhang, Y., Xu, C., Li, H., Yang, K., Cheng, N., & Shen, X. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.2975792 · Cited by 121 · see citing works
Fair Resource Allocation in Federated Learning
Li, T., Sanjabi, M., Beirami, A., & Smith, V. (2020) · International Conference on Learning Representations · Cited by 120 · see citing works
Toward Edge-Assisted Internet of Things: From Security and Efficiency Perspectives
Ni, J., Lin, X., & Shen, X. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800229 · Cited by 112 · see citing works
Real-Time Traffic Speed Estimation With Graph Convolutional Generative Autoencoder
Yu, J. J. Q., & Gu, J. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2910560 · Cited by 112 · see citing works
Self-citationMeeting points in ridesharing: A privacy-preserving approach
Aïvodji, U., Gambs, S., Huguet, M., & Killijian, M. (2016) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2016.09.017 · Cited by 104 · see citing works
Towards Rear-End Collision Avoidance: Adaptive Beaconing for Connected Vehicles
Lyu, F., Cheng, N., Zhu, H., Zhou, H., Xu, W., Li, M., & Shen, X. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2966586 · Cited by 98 · see citing works
An efficient privacy-preserving compressive data gathering scheme in WSNs
Xie, K., Ning, X., Wang, X., He, S., Ning, Z., Liu, X., Wen, J., & Qin, Z. (2017) · Information Sciences · doi:10.1016/j.ins.2016.12.050 · Cited by 84 · see citing works
Zhao, Y., Zhao, J., Jiang, L., Tan, R., & Niyato, D. T. (2019) · arXiv (Cornell University) · Cited by 79 · see citing works
Self-citationDelay Aware Power System Synchrophasor Recovery and Prediction Framework
Yu, J. J. Q., Lam, A. Y. S., Hill, D. J., Hou, Y., & Li, V. O. K. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2834543 · Cited by 78 · see citing works
Self-citationMeeting privacy challenges while advancing intelligent transportation systems
Fries, R. N., Gahrooei, M. R., Chowdhury, M., & Conway, A. J. (2012) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2012.04.002 · Cited by 68 · see citing works
Privacy-Preserving Transportation Traffic Measurement in Intelligent Cyber-physical Road Systems
Zhou, Y., Mo, Z., Xiao, Q., Chen, S., & Yin, Y. (2015) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2015.2436395 · Cited by 65 · see citing works
Privacy-Preserving Blockchain-Based Federated Learning for IoT DevicesPreprint
Zhao, Y., Zhao, J., Jiang, L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10893 · Cited by 54 · see citing works
Enabling Secure and Fast Indexing for Privacy-Assured Healthcare Monitoring via Compressive Sensing
Yuan, X., Wang, X., Wang, C., Weng, J., & Ren, K. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2602758 · Cited by 48 · see citing works
FedHealth: A Federated Transfer Learning Framework for Wearable HealthcarePreprint
Chen, Y., Wang, J., Yu, C., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.09173 · Cited by 45 · see citing works
Real-time privacy-preserving model-based estimation of traffic flows
Ny, J. L., Touati, A., & Pappas, G. J. (2014) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1109/iccps.2014.6843714 · Cited by 25 · see citing works
Freeway Performance Measurement System (PeMS)Preprint
Chen, C., & Varaiya, P. P. (2002) · RePEc: Research Papers in Economics · Cited by 24 · see citing works
Optimal privacy control for transport network data sharing
He, B. Y., & Chow, J. Y. J. (2019) · Transportation research procedia · doi:10.1016/j.trpro.2019.05.041 · Cited by 23 · see citing works
Optimal privacy control for transport network data sharing
He, B. Y., & Chow, J. Y. J. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.07.010 · Cited by 22 · see citing works
Madan, S., & Goswami, P. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3357276 · Cited by 13 · see citing works
Analysis of freeway traffic time series data and their application to incident detection
Ahmed, M. S. (1979) · SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University) · Cited by 7 · see citing works
Reliable Federated Learning for Mobile NetworksPreprint
Kang, J., Xiong, Z., Niyato, D. T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.06837 · Cited by 6 · see citing works
Self-citationChen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
Flagged first, then by citations
Research works printed but not linked: 2
- [47][47] Wagstaff, Cardie, Claire, Rogers, Seth, and Stefan, 'Constrained kmeans clustering with background knowledge,' ICML-2001 , 2001. -
- [50][50] A. Paszke, S. Gross, S. Chintala, and G. Chanan, 'Pytorch,' Computer software. Vers. 0.3 , vol. 1, 2017. -
Cited by
Sorted by citations
A survey on federated learning
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899 · see citing works
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · see citing works
“FL is also a very useful solution to replace traditional centralized ML approaches in traffic prediction tasks [10], [26] by running ML IoT Devices”
BackgroundInfluentialA survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480 · see citing works
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889 · see citing works
Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020) · IEEE Access · doi:10.1109/access.2020.3013541 · Cited by 733 · see citing works
Xu, W., Yang, Z., Ng, D. W. K., Levorato, M., Eldar, Y. C., & Debbah, M. A. (2023) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2023.3239189 · Cited by 681 · 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-citationFederated learning for 6G communications: Challenges, methods, and future directions
Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D. (2020) · China Communications · doi:10.23919/jcc.2020.09.009 · Cited by 464 · see citing works
“For example, industries such as remote surgery [14], cloud PLC [15], and intelligent transportation systems [16] will be empowered by the new service model to provide users with better services.”
BackgroundSelf-citationRahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
FedML: A Research Library and Benchmark for Federated Machine LearningPreprint
He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13518 · Cited by 504 · see citing works
Self-citationPrivacy-preserving blockchain-based federated learning for traffic flow prediction
Qi, Y., Hossain, M. S., Nie, J., & Li, X. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.12.003 · Cited by 339 · see citing works
Federated Learning in Smart City Sensing: Challenges and Opportunities
Jiang, J. C., Kantarcı, B., Oktuğ, S., & Soyata, T. (2020) · Sensors · doi:10.3390/s20216230 · Cited by 322 · see citing works
Federated Learning for Cybersecurity: Concepts, Challenges, and Future Directions
Alazab, M., Ramu, S. P., Parimala, M., Maddikunta, P. K. R., Gadekallu, T. R., & Pham, Q. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3119038 · Cited by 304 · see citing works
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315 · see citing works
Rahman, M. A., Hossain, M. S., Islam, M. S., Alrajeh, N. A., & Muhammad, G. (2020) · IEEE Access · doi:10.1109/access.2020.3037474 · Cited by 312 · see citing works
Federated learning for smart cities: A comprehensive survey
Pandya, S., Srivastava, G., Jhaveri, R. H., Babu, M. R., Bhattacharya, S., Maddikunta, P. K. R., Mastorakis, S., Piran, M. J., & Gadekallu, T. R. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102987 · Cited by 306 · see citing works
A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions
Aouedi, O., Vu, T.-H., Sacco, A., Nguyen, D. C., Piamrat, K., Marchetto, G., & Pham, Q. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3430368 · Cited by 318 · see citing works
Fusion of Federated Learning and Industrial Internet of Things: A survey
Boobalan, P., Ramu, S. P., Pham, Q., Dev, K., Pandya, S., Maddikunta, P. K. R., Gadekallu, T. R., & Huynh‐The, T. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109048 · Cited by 321 · see citing works
Shaygan, 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
Li, Y., Wang, R., Li, Y., Zhang, M., & Long, C. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120291 · Cited by 262 · see citing works
Federated learning: Overview, strategies, applications, tools and future directions
Yürdem, B., Kuzlu, M., Güllü, M. K., Çatak, F. Ö., & Tabassum, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38137 · Cited by 261 · see citing works
Bourechak, A., Zedadra, O., Kouahla, M. N., Guerrieri, A., Séridi, H., & Fortino, G. (2023) · Sensors · doi:10.3390/s23031639 · Cited by 295 · see citing works
PySyft: A Library for Easy Federated Learning
Ziller, A., Trask, A., Lopardo, A., Szymkow, B., Wagner, B., Bluemke, E., Nounahon, J.-M., Passerat-Palmbach, J., Prakash, K., Rose, N., et al. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-70604-3_5 · Cited by 246 · see citing works
Chellapandi, V. P., Yuan, L., Brinton, C. G., Żak, S. H., & Wang, Z. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3332675 · Cited by 249 · see citing works
Liu, S., Yu, J., Deng, X., & Wan, S. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3099368 · Cited by 234 · see citing works
Self-citationFederated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework With UAV Swarms
Liu, Y., Nie, J., Li, X., Ahmed, S. H., Lim, W. Y. B., & Miao, C. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3021006 · Cited by 227 · see citing works
Self-citationDeep learning for time series forecasting: a survey
Kong, X., Chen, Z., Liu, W., Ning, K., Zhang, L., Marier, S. M., Liu, Y., Chen, Y., & Xia, F. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02560-w · Cited by 279 · see citing works
Self-citation6G-Enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions
Chang, L., Zhang, Z., Li, P., Xi, S., Guo, W., Shen, Y., Xiong, Z., Kang, J., Niyato, D. T., Qiao, X., et al. (2022) · Journal of Communications and Information Networks · doi:10.23919/jcin.2022.9815195 · Cited by 204 · see citing works
Self-citationTang, F., Mao, B., Kawamoto, Y., & Kato, N. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3073009 · Cited by 198 · 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
Self-citationBlockchain for deep learning: review and open challenges
Shafay, M., Ahmad, R. W., Salah, K., Yaqoob, I., Jayaraman, R., & Omar, M. (2022) · Cluster Computing · doi:10.1007/s10586-022-03582-7 · Cited by 202 · see citing works
Machine learning for next-generation intelligent transportation systems: A survey
Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4427 · Cited by 179 · see citing works
Yuan, 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
Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges
Wang, S., Qureshi, M. A., Miralles-Pechuán, L., Huynh-The, T., Gadekallu, T. R., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3386872 · Cited by 170 · see citing works
We, Z., Wang, Z., & Li, X. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108885 · Cited by 170 · see citing works
Optimal Contract Design for Efficient Federated Learning With Multi-Dimensional Private Information
Ding, N., Fang, Z., & Huang, J. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036944 · Cited by 166 · see citing works
Federated Semisupervised Learning for Attack Detection in Industrial Internet of Things
Aouedi, O., Piamrat, K., Müller, G., & Singh, K. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3156642 · Cited by 165 · see citing works
Federated 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
Self-citationFederated Learning for IoT: A Survey of Techniques, Challenges, and Applications
Dritsas, E., & Τρίγκα, Μ. (2025) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan14010009 · 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
Privacy-Preserving Aggregation in Federated Learning: A Survey
Liu, Z., Guo, J., Yang, W., Fan, J., Lam, K., & Zhao, J. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3190835 · Cited by 153 · 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
IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues
Talebkhah, M., Sali, A. B., Marjani, M., Gordan, M., Hashim, S. J., & Rokhani, F. Z. (2021) · IEEE Access · doi:10.1109/access.2021.3070905 · Cited by 147 · see citing works
AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope
Singh, A. P., Satapathy, S. C., Roy, A., & Gutub, A. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06348-2 · Cited by 139 · see citing works
Efficient Federated Learning With Spike Neural Networks for Traffic Sign Recognition
Xie, K., Zhang, Z., Li, B., Kang, J., Niyato, D., Xie, S., & Wu, Y. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3178808 · Cited by 128 · see citing works
Self-citationAbbas, S., Issa, G. F., Fatima, A., Abbas, T., Ghazal, T. M., Ahmad, M., Yeun, C. Y., & Khan, M. A. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/2599161 · Cited by 122 · see citing works
Privacy-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
Self-citationChatterjee, P., Das, D., & Rawat, D. B. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.04.057 · Cited by 127 · see citing works
Huang, X., Li, P., Yu, R., Wu, Y., Xie, K., & Xie, S. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3098170 · Cited by 115 · see citing works
Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design
Lin, X., Wu, J., Bashir, A. K., Li, J., Yang, W., & Piran, M. J. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3024246 · Cited by 113 · see citing works
IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain
Samuel, O., Omojo, A. B., Onuja, A. M., Sunday, Y., Tiwari, P., Gupta, D., Hafeez, G., Yahaya, A. S., Fatoba, O. J., & Shamshirband, S. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3143576 · Cited by 113 · see citing works
Federated Learning-Based Misbehavior Detection for the 5G-Enabled Internet of Vehicles
Rani, P., Sharma, C., Ramesh, J. V. N., Verma, S., Sharma, R., Alkhayyat, A. H., & Kumar, S. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3328020 · Cited by 118 · see citing works
Zhang, L., Shen, B., Barnawi, A., Xi, S., Kumar, N., & Wu, Y. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10144-6 · Cited by 112 · see citing works
Xu, M., Peng, J., Gupta, B. B., Kang, J., Xiong, Z., Li, Z., & El‐Latif, A. A. A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3081626 · Cited by 110 · see citing works
Self-citationPerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks
Cao, X., Sun, G., Yu, H., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3172114 · Cited by 110 · see citing works
Reputation-Based Federated Learning for Secure Wireless Networks
Song, Z., Sun, H., Yang, H. H., Wang, X., Zhang, Y., & Quek, T. Q. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3079104 · Cited by 107 · see citing works
Federated Learning: Challenges, Methods, and Future Directions
Singh, P., Singh, M. K., Singh, R., & Singh, N. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-85559-8_13 · Cited by 106 · see citing works
Federated Self-Supervised Learning of Multisensor Representations for Embedded Intelligence
Saeed, A., Salim, F. D., Ozcelebi, T., & Lukkien, J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3009358 · Cited by 106 · see citing works
Thapa, C., Chamikara, M. A. P., & Camtepe, S. A. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_4 · Cited by 104 · see citing works
Khan, T. A., Fatima, A., Shahzad, T., Rahman, A., Alissa, K. A., Ghazal, T. M., Al-Sakhnini, M. M., Abbas, S., Khan, M. A., & Ahmed, A. (2023) · IEEE Access · doi:10.1109/access.2023.3266156 · Cited by 100 · see citing works
Zhang, X., Fang, F., & Wang, J. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3035807 · Cited by 99 · see citing works
Federated Self-Supervised Learning of Multisensor Representations for Embedded Intelligence
Saeed, A., Salim, F. D., Özçelebi, T., & Lukkien, J. J. (2024) · Figshare · Cited by 92 · see citing works
Federated learning for 6G-enabled secure communication systems: a comprehensive survey
Sirohi, D., Kumar, N., Rana, P. S., Tanwar, S., Iqbal, R., & Hijji, M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10417-3 · Cited by 98 · see citing works
BFOD: Blockchain-Based Privacy Protection and Security Sharing Scheme of Flight Operation Data
Li, X., Zhao, H., & Deng, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3296460 · Cited by 87 · 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
Huang, X., Yu, R., Ye, D., Shu, L., & Xie, S. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3064426 · Cited by 86 · see citing works
Qi, T., Chen, L., Li, G., Li, Y., & Wang, C. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110175 · Cited by 86 · see citing works
Federated learning for 5G base station traffic forecasting
Perifanis, V., Pavlidis, N., Koutsiamanis, R.-A., & Efraimidis, P. S. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109950 · Cited by 87 · see citing works
Optimizing Task Assignment for Reliable Blockchain-Empowered Federated Edge Learning
Kang, J., Xiong, Z., Li, X., Zhang, Y., Niyato, D., Leung, C., & Miao, C. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3055767 · Cited by 81 · see citing works
Self-citationFederated 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
Towards efficient communications in federated learning: A contemporary survey
Zhao, Z., Mao, Y., Liu, Y., Song, L., Ouyang, Y., Chen, X., & Ding, W. (2023) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2022.12.053 · Cited by 80 · see citing works
Self-citationSaha, S., Hota, A., Chattopadhyay, A. K., Nag, A., & Nandi, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10766-7 · Cited by 88 · see citing works
Semi-Supervised Federated Learning for Travel Mode Identification From GPS Trajectories
Zhu, Y., Liu, Y., Yu, J. J. Q., & Yuan, X. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3092015 · Cited by 79 · see citing works
Self-citationA comprehensive survey of federated transfer learning: challenges, methods and applications
Guo, W., Zhuang, F., Zhang, X., Tong, Y., & Dong, J. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40065-x · Cited by 85 · see citing works
Safe: Synergic Data Filtering for Federated Learning in Cloud-Edge Computing
Xu, X., Li, H., Li, Z., & Zhou, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3195896 · Cited by 76 · see citing works
Ogundokun, R. O., Misra, S., Maskeliūnas, R., & Damaševičius, R. (2022) · Information · doi:10.3390/info13050263 · 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
Liu, L., Tian, Y., Chakraborty, C., Feng, J., Pei, Q., Zhen, L., & Yu, K. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3280515 · Cited by 73 · see citing works
Comprehensive Android Malware Detection Based on Federated Learning Architecture
Fang, W., He, J., Li, W., Lan, X., Chen, Y., Li, T., Huang, J., & Zhang, L. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3287395 · Cited by 76 · see citing works
Deep learning serves traffic safety analysis: A forward‐looking review
Razi, A., Chen, X., Li, H., Wang, H., Russo, B. J., Chen, Y., & Yu, H. (2022) · IET Intelligent Transport Systems · doi:10.1049/itr2.12257 · Cited by 77 · see citing works
Federated Deep Reinforcement Learning for Efficient Jamming Attack Mitigation in O-RAN
Houda, Z. A. E., Moudoud, H., & Brik, B. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3359998 · Cited by 71 · see citing works
Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges
Pouriyeh, S., Shahid, O., Parizi, R. M., Sheng, Q. Z., Srivastava, G., Zhao, L., & Nasajpour, M. (2022) · Applied Sciences · doi:10.3390/app12188980 · Cited by 165 · see citing works
Advances in Traffic Congestion Prediction: An Overview of Emerging Techniques and Methods
Mystakidis, A., Koukaras, P., & Tjortjis, C. (2025) · Smart Cities · doi:10.3390/smartcities8010025 · Cited by 70 · see citing works
Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey
Liu, S., Guo, B., Fang, C., Wang, Z., Luo, S., Zhou, Z., & Yu, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3319952 · Cited by 73 · see citing works
Xu, Y., Bhuiyan, M. Z. A., Wang, T., Zhou, X., & Singh, A. K. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3149335 · Cited by 67 · 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
Integration of federated learning with IoT for smart cities applications, challenges, and solutions
Ghadi, Y. Y., Mazhar, T., Shah, S. F. A., Haq, I., Ahmad, W., Ouahada, K., & Hamam, H. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1657 · Cited by 66 · see citing works
Federated Learning: Navigating the Landscape of Collaborative Intelligence
Lazaros, K., Koumadorakis, D. E., Vrahatis, A. G., & Kotsiantis, S. B. (2024) · Electronics · doi:10.3390/electronics13234744 · Cited by 66 · see citing works
Zhang, Z., Xu, X., Gong, W., Chen, Y., & Gao, H. (2021) · Control Engineering Practice · doi:10.1016/j.conengprac.2021.104913 · Cited by 65 · see citing works
Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations
Liu, Q., Yang, B., Wang, Z., Zhu, D., Wang, X., Ma, K., & Guan, X. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3150182 · Cited by 63 · see citing works
Gao, B., Yang, F., Cui, N., Xiong, K., Lu, Y., & Wang, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3214211 · Cited by 63 · see citing works
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
Kumar, R., Khan, A. A., Kumar, J., Zakria, Golilarz, N. A., Zhang, S., Ting, Y., Zheng, C., & Wang, W. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3076767 · Cited by 57 · see citing works
Self-citationBlika, A., Palmos, S., Doukas, G. S., Lamprou, V., Pelekis, S., Kontoulis, M., Ntanos, C., & Askounis, D. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3449563 · Cited by 69 · see citing works
Communication Efficiency in Federated Learning: Achievements and ChallengesPreprint
Shahid, O., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Srivastava, G., & Zhao, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10996 · Cited by 62 · see citing works
Robust Semisupervised Federated Learning for Images Automatic Recognition in Internet of Drones
Zhang, Z., Ma, S., Yang, Z., Xiong, Z., Kang, J., Wu, Y., Zhang, K., & Niyato, D. T. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3151945 · Cited by 61 · see citing works
Self-citationDeep Federated Learning-Based Privacy-Preserving Wind Power Forecasting
Ahmadi, A., Talaei, M., Sadipour, M., Amani, A. M., & Jalili, M. (2022) · IEEE Access · doi:10.1109/access.2022.3232475 · Cited by 60 · see citing works
IOFL: Intelligent-Optimization-Based Federated Learning for Non-IID Data
Li, X., Zhao, H., & Deng, W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3354942 · Cited by 60 · see citing works
Failure prediction in production line based on federated learning: an empirical study
Ge, N., Li, G., Zhang, L., & Liu, Y. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01775-2 · Cited by 58 · see citing works
Self-citationYang, Q. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3485875 · Cited by 62 · see citing works
A Survey of Federated Learning for Connected and Automated Vehicles
Chellapandi, V. P., Yuan, L., Żak, S. H., & Wang, Z. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10421974 · Cited by 59 · see citing works
- Authors
- Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S.
- Affiliations
- Southern University of Science and Technology (CN)
- Nanyang Technological University (SG)
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-08
- Volume
- 7
- Issue
- 8
- Pages
- 7751-7763
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3010852232
- Cited by
- 712
- References
- 58
- Citations by year
- 2026: 67 · 2025: 122 · 2024: 157 · 2023: 140 · 2022: 116 · 2021: 85 · 2020: 25
- FWCI
- 35.97
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- General Program of Guangdong Basic and Applied Basic Research Foundation (2019A1515011032)
- Guangdong Provincial Key Laboratory (2020B121201001)
- National Research Foundation (NRF), Singapore
- Singapore Energy Market Authority, Energy Resilience (NRF2017EWT-EP003-041)
- Singapore NRF (NRF2015-NRF-ISF001-2277)
- Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE (DeSTSCI2019-0007)
- A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing (RGANS1906, WASP/NTU M4082187 (4080))
- Singapore MOE Tier 2 (MOE2014-T2-2-015 ARC4/15)
- MOE Tier 1 (2017-T1-002-007 RG122/17)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- traffic flow prediction, federated learning, privacy preservation, gated recurrent unit, FedAvg, consensus clustering, communication cost reduction, data privacy
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
