Cited by 713Privacy-Preserving Technologies in DataTraffic Prediction and Management TechniquesInternet Traffic Analysis and Secure E-voting

The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.

Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

Open access

Liu, Yi, Yu, James J. Q., Kang, Jiawen, Niyato, Dusit, Zhang, Shuyu·2020·IEEE Internet of Things Journal

DOI 10.1109/jiot.2020.2991401

Open this paper in CoChat — free account Open PDF

Abstract

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

Crossref · 10.1109/jiot.2020.2991401

Cites

62 of 62 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
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
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
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
A survey on transfer learning
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
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
Self-citation
Federated 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
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
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317

Show all 58 references

Research works printed but not linked: 1

zhao, 2018, Federated learning with non-IID data

Sorted by citations

Items 601–700 of 713

Research on Higher Education System: An AHP-TOPSIS Algorithm and Damping Trend Model-based Approach
Ping, Y., Jiang, L., & Zhang, Y. (2021) · International Conference on Artificial Intelligence and Information Systems (ICAIIS) · doi:10.1145/3469213.3469228
Food System Driven by Obstacle Degree Optimization Algorithm: System Design and Algorithm Analysis
Gao, R., Wang, J., & Chen, Z. (2021) · International Conference on Artificial Intelligence and Information Systems (ICAIIS) · doi:10.1145/3469213.3469227
Predicting traffic flow during peak commuting hoursPreprint
Shen, Z. (2021) · Research Square · doi:10.21203/rs.3.rs-877585/v1
An improved 4D trajectory modeling approach for traffic managementPreprint
Shen, Z., & Yu, X. (2021) · Research Square · doi:10.21203/rs.3.rs-838693/v1
Listen-on-Edge: towards Learning Realtime Traffic Flow in Smart City
Hu, L., Yang, Y., Sun, G., Duan, R., & Ding, X. (2021) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom53800.2021.00032
Traffic state prediction based on stochastic repetitive hill climbing methodPreprint
Shen, Z. (2021) · Research Square · doi:10.21203/rs.3.rs-1006289/v1
Short-time traffic flow prediction based on seasonal gray Fourier modelPreprint
Shen, Z. (2022) · Research Square · doi:10.21203/rs.3.rs-1334110/v1
Research Model of Optimal Catalytic Combination Based on Machine Learning
Peng, J., Zhu, Z., & Tian, Y. (2021) · International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) · doi:10.1145/3495018.3501166
Design of Dynamic Pricing Algorithm for Supply Chain: A Game Optimization Method
Li, R., Zhang, Z., & Zhang, D. (2022) · Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) · doi:10.1145/3544109.3544333
School bus path planning problem based on improved simulated annealing algorithm
Huang, X., Hu, Z., & Zhou, B. (2022) · Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) · doi:10.1145/3544109.3544334
Using a speed-gated spatiotemporal graph convolution network to forecast traffic flowPreprint
Shen, Z. (2022) · Research Square · doi:10.21203/rs.3.rs-2044494/v1
Discrete Wavelet Transform-Graph Convolution Network-based Short-Term Traffic Flow ForecastingPreprint
Shen, E. Z. (2022) · Research Square · doi:10.21203/rs.3.rs-2133688/v1
Unsupervised domain adaptive semantic segmentation of road scenes based on positional adversarial learningPreprint
Shen, E. Z. (2022) · Research Square · doi:10.21203/rs.3.rs-2141497/v1
Towards an Efficient, Privacy-aware Federated Learning SchemePreprint
Alekszejenkó, L., & Dobrowiecki, T. (2022) · Research Square · doi:10.21203/rs.3.rs-2072660/v1 · Cited by 1
Research on Real-time Monitoring of Video Images of Traffic Vehicles and Pedestrian Flow using Intelligent Algorithms
Dong, X., Lan, J., & Wu, W. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0131271
Application of artificial intelligence in secure decentralized computation enabled by TOTEM
Jose, D. T., Rong, C., & Chakravorty, A. (2022) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde56538.2022.10089248
Floresta de Decisão Distribuída: Um Sistema de Aprendizado de Máquina Colaborativo Par-a-Par para Detecção de Intrusão em Redes
Pereira, L. F. L., Moraes, I. M., & Mattos, D. M. F. (2023) · Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC) · doi:10.5753/sbrc.2023.469
Traffic Flow Distribution Forecast with Federated Learning and Bayesian Deep LearningPreprint
Shen, Z. E. (2023) · Research Square · doi:10.21203/rs.3.rs-3176684/v1
Origin-Destination Demand Prediction for Ride-sharing with Deep Bayesian LearningPreprint
Zhou, S. E. (2023) · Research Square · doi:10.21203/rs.3.rs-3176716/v1
A Novel Privacy-Aware Traffic Speed Prediction Model Based on Recurrent Neural NetworksPreprint
Akın, M., Canbay, Y., & Sağıroğlu, Ş. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4591259
RETRACTED ARTICLE: Enhancing throughput using channel access priorities in frequency hopping network using federated learning
Han, Y., & Zhu, X. (2023) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-023-02313-9
Retracted
A Traffic Flow Prediction Method Based on Blockchain and Federated LearningPreprint
Zhi, H., 段, 苗., & Yang, L. (2023) · Research Square · doi:10.21203/rs.3.rs-3419449/v1
Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm
Patel, S., & Patel, R. M. (2023) · American Journal of Bioscience and Bioengineering · doi:10.11648/j.bio.20231105.14
Accurate and Decentralized Approach for Traffic Flow Prediction Using Federated Learning
Maurya, M. K., & Kumar, M. (2023) · International Computer Science and Engineering Conference · doi:10.1109/icsec59635.2023.10329634
Federated Learning for IoT Applications, Attacks and Defense Methods
Qu, Z., Duan, R., Liu, Y., & Lu, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-42637-7_9
Self-citation
Real-time Road Anomaly Detection using Advanced Sensor NetworksPreprint
Rahmani, A., Al-Farsi, F., Chen, W. R., & Shen, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-3914460/v1
Synthetic data as a contribution and a reward in multi-agent urban traffic forecasting
Pavlyuk, D., & Perevozcikova, J. (2024) · Transportation research procedia · doi:10.1016/j.trpro.2024.02.017
A Brief Overview of Federated Learning - A New Perspective on Data Privacy
Lipovanu, I., Pascal, C. M., & Caruntu, C. F. (2022) · Buletinul Institutului Politehnic din Iaşi. Secţia Electrotehnică. Energetică. Electronică · doi:10.2478/bipie-2022-0019
Combining Active Privacy Clustering and Knowledge Merge for Transferable Federated Learning in Wind Power PredictionPreprint
Cong, F., Wu, R., Zhong, W., & Lin, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4778866
Fil-Cad: A Fault Detection Method for Iiot Based on Federated Incremental Learning with Class Accuracy DistillationPreprint
Liu, Y., Fang, W., Huang, W., Wang, X., Zhao, B., & Liu, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4822139
Self-citation
BTD-GTAN: Federated Traffic Flow Prediction with Multimodal Feature Fusion Considering Anomalies
Feng, S., Feng, X., Xu, L., & Zheng, H. (2024) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs61882.2024.10603032
Backpropagation-Based Deep Learning Model for Privacy-Preserving of Confidential Data
Soni, M., & Singh, D. K. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-0037-0_37
Individualized Federated Learning for Traffic Prediction with Error-Driven Aggregation
Chen, H., Meese, C., Nejad, M., & Shen, C. (2026) · ACM Journal on Autonomous Transportation Systems · doi:10.1145/3796508 · Cited by 2
Blockchain-based Federated Learning System for Intelligent Traffic Management
Du, J., Wang, X., Wu, Q., & Wang, C. (2024) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds62779.2024.10667585
Confidential Spatio-Temporal Graph Convolutional Networks with Differential Privacy for Traffic Forecasting
Chen, X., Xiao, A., Pang, W., Jiang, X., Ren, Z., & Tang, M. (2024) · International Conference on Electronic Communication and Artificial Intelligence (ICECAI) · doi:10.1109/icecai62591.2024.10674876
FedDGCL: Federated Graph Neural Network with Dual Graph Contrast Learning for Multivariable Time Series Forecasting
Zhou, Y., Yu, G., Guo, F., Jing, F., Yang, J., & Bie, R. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5552-3_27
IIoT: Traffic Data Flow Analysis and Modeling Experiment for Smart IoT Devices
Bhardwaj, A. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815256710124010008
Vehicular Traffic Flow Prediction via Decentralized Federated Meta-Learning
Selvia, A., Singh, A., & Wu, W. (2024) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice62917.2024.00016
Advanced Architectures and Innovative Platforms for Federated Learning: A Comprehensive Exploration
Bhati, N., & Vyas, N. (2024) · doi:10.1002/9781394219230.ch8
Efficient Shapley Value Driven Federated Learning System for Smart Grid
Wu, Q., Pan, Y., Mei, F., & Lu, M. (2024) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics62053.2024.10796417
An Accurate and Efficient Clustered Federated Learning for Mobile Edge Devices
Shubha, S. S., & Shen, H. (2024) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec62691.2024.00017
MPC-friendly Vertical Federated Learning for Electricity Consumption Prediction in Smart Grids
Wu, Q., Li, Y., Zhao, R., & Lu, M. (2024) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit62299.2024.10828066
FL-RDGRU: A Federal Learning Traffic Flow Prediction Without Memory Loss
Li, B., Wang, X., & Li, J. (2024) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice63571.2024.10864317
FedNHN: Generated Data-driven Personalized Federated Learning with Heterogeneous Data
Sun, J., Dong, J., Su, Z.-E., Zhang, W., & Zhang, K. (2024) · Globecom · doi:10.1109/globecom52923.2024.10901645
Privacy-Preserving Multi-period Traffic Prediction Model
Sun, S., Zhu, Z., Zhang, X., & Song, Z. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5373-7_50
Intelligent Block Data Storage in Electronic Health Repositories: AI-Driven Recommendation System
Kaur, K., Singh, S., Nirsanametla, Y., Kumar, P., Bisht, Y. S., & Habelalmateen, M. I. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-2528-4_24
An Enhanced Traffic Sign Recognition Approach Based on Federated Learning
Shahria, M., Hasan, M. K., Khaliluzzaman, M., Barua, C., & Uddin, M. B. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11021907
Distributed optimization with faulty nodes: robust aggregation in hyperbolic space
Ghosh, S. K., & Vittamsetti, V. M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11475-0
Learning to Optimize with Distributional Shift Constraints: A Novel Framework for Safe Domain Adaptation in Machine LearningPreprint
Li, W., Ying, Z., Feng, C., Liu, J., Qian, Z., Li, Z., & Zhou, S. (2025) · Research Square · doi:10.21203/rs.3.rs-7250445/v1
Dual-Scale Traffic Management for Differentiated Services in Satellite Mega Constellations
Zhou, D., He, Y., Sheng, M., Fu, S., Li, J., & Han, Z. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3601348
Privacy-Preserving Sketch-Based Big Data Traffic Detection
Zhang, C., Liu, Y., Ren, X., Xu, J., Wang, Y., & Zhu, L. (2025) · International Conference on Big Data and Privacy Computing (BDPC) · doi:10.1109/bdpc63545.2025.11135568
Self-citation
FedTK: A Method for Non-IID Fed Learning via Trace-Kernel and Robust Aggregation
Dong, J., Hou, K., Xu, Q., Zhang, K., Su, Z.-E., & Fang, S. (2025) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs66275.2025.00074
FedMCP: A Personalized Federated Learning Based on Kernel Distance Between Clients
Xia, Z., Fang, S., Liu, Y., Jia, R., Zhang, K., & Zhang, L. (2025) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs66275.2025.00075
Self-citation
Privacy-Preserving Misbehavior Detection in IoV using Federated Learning and Blockchain
Veni, M. H. N., Kumar, M. A., & Reddy, P. A. (2025) · International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3) · doi:10.1109/isac364032.2025.11156934
AHFL: A Resource-Adaptive Approach for Data-Heterogeneity-Aware Federated Learning
Yang, Y. L., Zhu, S., Sun, G., & Zheng, W. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3610814
Enhancing Privacy with Attribute-Centric Anonymization and Synthetic Data Approaches: Comprehensive Reviews and Innovative Solutions
Khivsara, B. A., & Rathore, M. (2025) · Journal of The Institution of Engineers (India) Series C · doi:10.1007/s40032-025-01244-2
Convergence Analysis and Optimisation of Privacy-Preserving Federated Learning for Hierarchical Graph Neural Networks in Distributed Cloud Anomaly Detection
Lawal, C., Olaniyan, O. M., Okokpujie, K., & Adetiba, E. (2025) · Computation · doi:10.3390/computation13120283
Exploring and Evaluating Deep Learning Techniques for Traffic Prediction in Urban Environments
Buchade, S., & Sakarkar, G. (2025) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981251394676
FedNash: A Nash Equilibrium-Based Federated Multi-Task Learning Framework for Internet of Vehicles
Li, B., Wang, X., Wu, Q., & Wang, C. (2025) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1109/aiahpc66801.2025.11290046
Retracted: Privacy-Preserving Federated Learning Algorithm for Distributed Health Data Analysis
Hundekari, S., Praveen, R., Shrivastava, A., Hwsein, R. R., Bansal, S., Hussain, H. A., Kadhim, H. M., & Jumaili, M. L. F. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292130
Retracted
Differential Privacy-Preserving GANs for De-Identification of Medical Images in Federated Health Networks
Ismail, T. Z., Abushraida, A. A. J., Saini, R., & Kholmatovna, U. N. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292474
Privacy-Preserving Aggregation in IoT Sensor Networks via Federated Averaging Algorithms
Agarwal, T., Al-Dmour, N. A., & Ghazal, T. M. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292234
Federated Learning for Multiple Personalized Tasks in Internet of Vehicles
Li, B., Fu, Y., Tang, X., & Li, C. (2025) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2025-fall65116.2025.11310086
Privacy-Preserving Student Success Prediction Using a Federated Weighted Ensemble Under Non-IID Data
Kumar, A., & Sinha, R. K. (2025) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig68706.2025.11323777
An Adaptive Communication Efficient Federated Learning Framework for Edge Intelligence under Resource and Privacy Constraints
Sikarwal, P. K., Raskar, S. D., Gupta, M. K., Gupta, A., Bhardwaj, M. K., & Tiwari, A. (2025) · International Conference on Computing, Communication and Automation · doi:10.1109/iccca66364.2025.11325591
A Study on Privacy-Focused Machine Learning in IoT Networks via Federated Learning
Jisna Jaison, T., & Thilakavathy, P. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330425
Case Studies and Research Insights
Pei, J., Wang, L., & Dai, M. (2026) · SpringerBriefs in computer science · doi:10.1007/978-981-95-6160-5_4
Transport Timeliness Evaluation Using Gaussian Process Regression in Smart City Transit Systems
Rahim, M. A., Avdeeva, A. N., Sriram, B., Kizi, I. Y. A., Saini, R., & Younus, Y. M. (2025) · International Conference on AI-Driven STEM Education and Learning Technologies (AISTEMEDU) · doi:10.1109/aistemedu67077.2025.11403902
Urban Traffic Flow Simulation Using Cellular Automata in Smart City Transportation Networks
Azimov, J., Ugli, G. A. N., Guru, K. V., Sachdeva, L., & Kumar, S. D. (2025) · International Conference on AI-Driven STEM Education and Learning Technologies (AISTEMEDU) · doi:10.1109/aistemedu67077.2025.11403905
SLMFORGE: Small Language Models for Federated Feature Selection via Union Aggregation in Cybersecurity
Sheikhi, S. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401511 · Cited by 1
Blockchain based semi-Decentralized personalized federated learning
Liu, Y., Lu, Y., Zhu, G., Liu, S., Li, R., Yang, K., & Wang, J. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112197
Self-citation
Silent-App-Aware Federated Machine Unlearning for Encrypted Network Traffic Classification
Li, Z., Jiang, Y., Wang, T., Wang, P., & Ji, Y. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3672152
STDFL: A Spatio-Temporal-Aware Dynamic Federated Learning Framework for Spatial Crowdsourcing
Li, M., Wang, Z., Ma, C., Jia, C., Qi, L., & Du, R. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3673177
N Truths and a Lie: Consistency-Based Backdoor Defense for Vertical Federated Learning
Ma, X., Zhang, Z., He, K., Chen, J., & Du, R. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3675464
FML-TFP: A Federated Meta-Learning Approach for Distributed Traffic Flow Prediction
Gao, P., Fan, Q., Zhang, M., Liang, Y., Li, G., Su, S., & Liu, L. (2025) · Globecom · doi:10.1109/globecom59602.2025.11432635
Empowering IoT with Federated Learning: A Cross-Domain Survey from Healthcare to Industry
Aggarwal, S., Purkayastha, A. A., & Tharwani, J. (2026) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi68868.2026.11465303
Secure collaborative management platform applied to IoT Networks
Atia, O. B. (2024) · theses.fr (ABES) · doi:10.70675/6c43d453z9fb0z4a37z8123z35304e4a31cc
Federated learning models for remaining useful life estimation - Application to reflectometry-based prognostics of electrical cables
Naline, B. (2024) · theses.fr (ABES) · doi:10.70675/bd62b94dz1bfbz40e3za054zc5faeb8ecb7a
Flexible Privacy-Preserving Cardinality Query for Massive Distributed Datasets
Xie, D., Wang, P., Yang, C., Chen, T., Zhao, J., & Guan, X. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3687031
Fine-grained Federated Traffic Prediction Model Based on Heterogeneous Pattern Representation Learning
Sun, S. Y., Chen, B., Jiang, X., Zhang, X., Song, Z., & Yang, L. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11487341
Genetic algorithm-driven aggregation for federated learning in 6G-enabled smart cities
Fouda, R., Zidi, S., Bennaji, N., & Zidi, I. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21431-2
Dynamic cooperative search optimized multi-level attention-based reinforcement learning for secure data offloading in internet of vehicles
Dhanare, R., Gupta, J., & Mujawar, S. S. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111245
Interpretable reputation driven asynchronous consensus vehicle networking federated learning architecture
Zi, Y., & Zhou, Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-53140-z
Privacy Preservation in Textual Data: A Systematic Mapping Study on Differential Privacy and Semantic Similarity
Lim-Apo, D. L., & Canedo, E. D. (2026) · Congresso Ibero-Americano em Engenharia de Software (CIbSE) · doi:10.5753/cibse.2026.42448
FedTETP: Federated learning with topology enhancement for traffic prediction
Wang, X., Chen, C., Chen, J., Jin, B., Zou, F., Liao, L., & Zeng, R. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108641
Privacy-Preserving Federated Learning for Post-Flood Security and Resource Planning in Bangladesh: A Review and Conceptual Framework
Druba, R. A., Shihab, A. H., Mussa, A., & Мансурова, М. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11546266
Federated learning applications in 6G communications and smart societies
Wahid, R. R., & Azzedin, F. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-344433-3.00023-x
Privacy-Preserving Distributed Estimation via Compressive Diffusion LMS for Secure IoT
Xu, T., Zhang, J. Q., He, X., Zhang, H., & Long, X. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3705261
Initialisation Strategies and Convergence Behaviour in Federated YOLOV8-Based Object Detection for Intelligent Transportation Systems
Akuma, C. F., Bren, T., Iwendi, C., Uwah, S. E., Sharma, V., & Raji, M. N. (2026) · International Conference on Connected Intelligence for Industrial Applications (CI2A) · doi:10.1109/ci2a69097.2026.11576985
Communication-Efficient Federated Traffic Prediction via Hierarchical Split Learning
Yu, T., Kim, J., & Byun, J. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3705379
Fog Alliances: A Decentralized Cloud Structure with Federated Machine Learning for Citiverses
Thang, D. V., & Volkov, A. (2026) · Proceedings of Telecommunication Universities · doi:10.31854/1813-324x-2026-12-3-81-97
Federated Learning-Based Multi-Layer Security and Energy Intelligence Framework for Smart Homes
Suman, S., & Pv, M. (2026) · International Conference on Next Generation Electronics (NELEX) · doi:10.1109/nelex69209.2026.11590334
Federated, Prediction-Triggered Gateway Load Control for 5G/6G Edge Networks
Ndiaye, I., & Güneş, M. (2026) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom69760.2026.11598102
Fuzzy Federated Graph Learning via TSK Message Passing and Contrastive Uncertainty Learning
Liu, Z., Yu, H., Liao, Y., & Luo, X. (2026) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2026.3713494
Device Assignment and Model Splitting Optimization for Resilient and Secure Multi-Hop Split Learning
Wei, D., Zhou, D., Liu, Y., & Chen, M. (2026) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2026.3713859
Self-citation
AI-driven multi-tier aerial communication networks: a review of routing, computing, handover, resource management, and optimization techniques
Siam, S. K., Arafat, M. Y., Alam, M. M., Zhu, X., Yang, P., Liu, Z., Guha, K., & Noor‐A‐Rahim, M. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11654-y
FedTraffic: A Hierarchical Federated Learning Framework for Traffic Flow Prediction in Intelligent Transportation Systems
Abboud, C., & Khalil, S. (2026) · Eng—Advances in Engineering · doi:10.3390/eng7080362
Security prediction and reliability analysis method of smart grid based on edge computing
Li, Q., Liu, S., Yu, J., He, T., Guo, C., Song, Y., Zhang, S. J., & Luo, Y. (2026) · Frontiers in Physics · doi:10.3389/fphy.2026.1855044
Self-citation
Enhancing Cloud Network Security: Utilizing Blockchain for Attack Detection Systems
SatishBabu, E., & Chowdary, S. (2026) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc69250.2026.11625128
Generative AI in urban science and practice
Zhang, Y., Xu, F., Wang, Q., Moro, E., Yue, Y., Wang, H., Чэн, Б., Liu, Y., Fang, D., Gong, P., et al. (2026) · Nature Cities · doi:10.1038/s44284-026-00492-2 · Cited by 1
Self-citation

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)
ORCID: Liu, Yi
Yu, James J. Q.
Kang, Jiawen
Niyato, Dusit
Zhang, Shuyu
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-08
Volume: 7
Issue: 8
Pages: 7751-7763
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2020.2991401
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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
Social Sciences › Transportation › 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

Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks