Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

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B 2 SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction

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Decision Tree-Based Federated Learning: A Survey

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Multiple households energy consumption forecasting using consistent modeling with privacy preservation

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Traffic Flow Prediction Based on Federated Learning and Spatio-Temporal Graph Neural Networks

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Privacy-preserving federated learning for proactive maintenance of IoT-empowered multi-location smart city facilities

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A Systematic Literature Review on Federated Learning: From A Model Quality Perspective

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A contemplative perspective on federated machine learning: Taxonomy, threats & vulnerability assessment and challenges

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A Game-Theoretic Federated Learning Framework for Data Quality Improvement

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Affordable federated edge learning framework via efficient Shapley value estimation

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Federated Learning Based Spatio-Temporal Framework for Real-Time Traffic Prediction

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Federated Transfer Learning for Privacy-Preserved Cross-City Traffic Flow Prediction

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Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

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Perturbation-enabled Deep Federated Learning for Preserving Internet of Things-based Social Networks

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Survey of Federated Learning Models for Spatial-Temporal Mobility Applications

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Digital Twin-Enabled Efficient Federated Learning for Collision Warning in Intelligent Driving

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Heuristic based federated learning with adaptive hyperparameter tuning for households energy prediction

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Differentially Private Federated Learning: A Systematic Review

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Privacy protection against attack scenario of federated learning using internet of things

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A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

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The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

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A profile similarity-based personalized federated learning method for wearable sensor-based human activity recognition

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Meta-heuristic federated learning aggregation methods for load forecasting

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Federated learning for intelligent fault diagnosis based on similarity collaboration

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Blockchain and Deep Learning Integration for Various Application: A Review

Afaq, Y., & Manocha, A. (2023) · Journal of Computer Information Systems · doi:10.1080/08874417.2023.2173330 · Cited by 23

Joint Edge Server Selection and Data Set Management for Federated-Learning-Enabled Mobile Traffic Prediction

Kim, D., Shin, S., Jeong, J., & Lee, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3301019 · Cited by 22

LightTR: A Lightweight Framework for Federated Trajectory Recovery

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A comprehensive survey and taxonomy on privacy-preserving deep learning

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A systematic review on federated learning system: a new paradigm to machine learning

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Multi-Modal Learning-Based Multi-Task Offloading Schemes for Satellite-Ground Integrated Networks

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Recent Advances in Federated Learning for Connected Autonomous Vehicles: Addressing Privacy, Performance, and Scalability Challenges

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Joint UAV Location and Resource Allocation for Air-Ground Integrated Federated Learning

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Partially-federated learning: A new approach to achieving privacy and effectiveness

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Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning

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Modeling Spatio-Temporal Mobility Across Data Silos via Personalized Federated Learning

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Predicting ride-hailing passenger demand: A POI-based adaptive clustering federated learning approach

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Personalized driving assistance algorithms: Case study of federated learning based forward collision warning

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Predictive Analysis and Simulation of College Sports Performance Fused with Adaptive Federated Deep Learning Algorithm

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GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation

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IIoT: Traffic Data Flow Analysis and Modeling Experiment for Smart IoT Devices

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A hybrid tuple selection pipeline for smartphone based Human Activity Recognition

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Train a central traffic prediction model using local data: A spatio-temporal network based on federated learning

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Facing spatiotemporal heterogeneity: A unified federated continual learning framework with self-challenge rehearsal for industrial monitoring tasks

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Toward data efficient anomaly detection in heterogeneous edge–cloud environments using clustered federated learning

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Smart Road Traffic Monitoring: Unveiling the Synergy of IoT and AI for Enhanced Urban Mobility

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Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles

Liu, M. (2021) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2021-fall52928.2021.9625535 · Cited by 19

Inferring Intersection Traffic Patterns With Sparse Video Surveillance Information: An ST-GAN Method

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Toward Efficient Data Trading in AI Enabled Reconfigurable Wireless Sensor Network Using Contract and Game Theories

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Towards a Personalized Movie Recommendation System: A Deep Learning Approach

Qin, Z., & Zhang, M. (2021) · International Conference on Artificial Intelligence and Information Systems (ICAIIS) · doi:10.1145/3469213.3470423 · Cited by 18

A Framework for Sustainable Federated Learning

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Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design

Zhang, Z., Ma, S., Nie, J., Wu, Y., Qiang, Y., Xu, X.-K., & Niyato, D. T. (2021) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys53884.2021.00047 · Cited by 18

Multi-Slice Privacy-Aware Traffic Forecasting at RAN Level: A Scalable Federated-Learning Approach

Phyu, H. P., Stanica, R., & Naboulsi, D. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3267725 · Cited by 18

Delay-Constrained Client Selection for Heterogeneous Federated Learning in Intelligent Transportation Systems

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Spatial-temporal graph neural network based on gated convolution and topological attention for traffic flow prediction

Bai, D., Xia, D., Huang, D., Hu, Y., Li, Y., & Li, H. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05053-x · Cited by 18

Deep learning enabled blockchain based electronic heathcare data attack detection for smart health systems

Chidambaranathan, S., & Geetha, R. (2023) · Measurement Sensors · doi:10.1016/j.measen.2023.100959 · Cited by 18

Federated Learning-Based Privacy-Preserving and Security: Survey

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Mobility-aware decentralized federated learning with joint optimization of local iteration and leader selection for vehicular networks

Chen, D., Deng, T., Jia, J., Feng, S., & Yuan, D. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111232 · Cited by 23

Smart Cities: A Systematic Review of Emerging Technologies

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Federated Spatio-Temporal Traffic Flow Prediction Based on Graph Convolutional Network

Wang, H., Zhang, R., Cheng, X., & Yang, L. (2022) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp55476.2022.10039323 · Cited by 21

Personalized Federated Learning for Automotive Intrusion Detection Systems

Shibly, K. H., Hossain, M. D., Inoue, H., Taenaka, Y., & Kadobayashi, Y. (2022) · IEEE Future Networks World Forum (FNWF) · doi:10.1109/fnwf55208.2022.00101 · Cited by 17

GA Approach to Optimize Training Client Set in Federated Learning

Kang, D., & Ahn, C. W. (2023) · IEEE Access · doi:10.1109/access.2023.3304368 · Cited by 17

Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying

Bian, J., & Xu, J. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3384061 · Cited by 23

Decision Trees in Federated Learning: Current State and Future Opportunities

Heiyanthuduwage, S. R., Altas, I., Bewong, M., Islam, M. Z., & Deho, O. B. (2024) · IEEE Access · doi:10.1109/access.2024.3440998 · Cited by 18

Comparison of De-Identification Techniques for Privacy Preserving Data Analysis in Vehicular Data Sharing

Löbner, S., Tronnier, F., Pape, S., & Rannenberg, K. (2021) · ACM Computer Science in Cars Symposium (CSCS) · doi:10.1145/3488904.3493380 · Cited by 17

Cooperative Location-Sensing Network Based on Vehicular Communication Security Against Attacks

Wang, Z., Wang, S., Bhuiyan, M. Z. A., Xu, J., & Hu, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3160453 · Cited by 16

RCFL: Redundancy-Aware Collaborative Federated Learning in Vehicular Networks

Hui, Y., Hu, J., Cheng, N., Zhao, G., Chen, R., Luan, T. H., & Aldubaikhy, K. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3336823 · Cited by 16

Privacy-preserving culvert predictive models: A federated learning approach

Mohammadi, P., Rashidi, A., & Asgari, S. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102483 · Cited by 16

Distributed Short-Term Traffic Flow Prediction Based on Integrating Federated Learning and TCN

Liu, T., Wang, Y., Zhou, H., Luo, J., & Deng, F. (2024) · IEEE Access · doi:10.1109/access.2024.3474300 · Cited by 16

Truthful Incentive Mechanism Design via Internalizing Externalities and LP Relaxation for Vertical Federated Learning

Lu, J., Pan, B., Seid, A. M., Li, B., Hu, G., & Wan, S. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3227270 · Cited by 17

A Short-Term Traffic Flow Prediction Method Based on Personalized Lightweight Federated Learning

Dai, G., & Tang, J. (2025) · Sensors · doi:10.3390/s25030967 · Cited by 16

Congestion Forecasting Using Machine Learning Techniques: A Systematic Review

Attioui, M., & Lahby, M. (2025) · Future Transportation · doi:10.3390/futuretransp5030076 · Cited by 15

Neurological disease prediction using impaired gait analysis for foot position in cerebellar ataxia by ensemble approach

Sundari, M. S., & Jadala, V. C. (2023) · Automatika · doi:10.1080/00051144.2023.2194097 · Cited by 16

FedSSC: Joint client selection and resource management for communication-efficient federated vehicular networks

Liu, S., Guan, P., Yu, J., & Taherkordi, A. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110100 · Cited by 18

Local-Global Spatial-Temporal Graph Convolutional Network for Traffic Flow Forecasting

Zong, X., Chen, Z., Yu, F., & Wei, S. (2024) · Electronics · doi:10.3390/electronics13030636 · Cited by 16

FLCSDet: Federated Learning-Driven Cross-Spatial Vessel Detection for Maritime Surveillance With Privacy Preservation

Huang, Y., Liu, R. W., Lin, Y., Kang, J., Zhu, F., & Wang, F. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3488497 · Cited by 17

Privacy-Preserving Graph Inference Network for Multi-Entity Wind Power Forecast: A Federated Learning Approach

Long, X., Ding, Y., Li, Y., Li, Y., Gao, L., & Zeng, Z. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3547227 · Cited by 15

An Enhanced Motif Graph Clustering-Based Deep Learning Approach for Traffic Forecasting

Zhang, C., Zhang, S., Yu, J. J. Q., & Yu, S. (2020) · Globecom · doi:10.1109/globecom42002.2020.9322104 · Cited by 15

Federated Multi-Discriminator BiWGAN-GP Based Collaborative Anomaly Detection for Virtualized Network Slicing

Wang, W., Liang, C., Tang, L., Yanıkömeroğlu, H., & Chen, Q. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3200059 · Cited by 14

A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

Firdaus, M., Larasati, H. T., & Rhee, K.-H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.039487 · Cited by 16

Advancing Medical Recommendations With Federated Learning on Decentralized Data: A Roadmap for Implementation

Kumari, R., Kumar, S., Gupta, S., Cengiz, K., & Ivković, N. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3334159 · Cited by 14

Secure and Efficient Federated Learning for Robust Intrusion Detection in IoT Networks

Houda, Z. A. E., Moudoud, H., & Khoukhi, L. (2023) · Globecom · doi:10.1109/globecom54140.2023.10436768 · Cited by 14

Overcoming intergovernmental data sharing challenges with federated learning

Sprenkamp, K., Fernández, J. D., Eckhardt, S., & Zavolokina, L. (2024) · Data & Policy · doi:10.1017/dap.2024.19 · Cited by 18

Energy-Efficient Wireless Resource Allocation for Heterogeneous Federated Multitask Networks Based on Evolutionary Learning

Jiang, B., Cai, L., Yue, G., Luo, F., Li, S., & Wang, J. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3538096 · Cited by 14

RC-SSFL: Towards Robust and Communication-efficient Semi-supervised Federated Learning System.Preprint

Liu, Y., Yuan, X., Zhao, R., Zheng, Y., & Zheng, Y. (2020) · arXiv (Cornell University) · Cited by 14

A Systematic Literature Review of Traffic Congestion Forecasting: From Machine Learning Techniques to Large Language Models

Attioui, M., & Lahby, M. (2025) · Vehicles · doi:10.3390/vehicles7040142 · Cited by 13

An ensemble federated learning framework for privacy-by-design mobility behaviour inference in smart cities

Badu-Marfo, G., Farooq, B., Mensah, D., & Mallah, R. A. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104703 · Cited by 13

Federated Learning With Heterogeneous Quantization Bit Allocation and Aggregation for Internet of Things

Chen, S., Li, L., Wang, G., Pang, M., & Shen, C. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3296493 · Cited by 13

Resource management in multi-heterogeneous cluster networks using intelligent intra-clustered federated learning

Mughal, F. R., He, J., Zhu, N., Hussain, S., Zardari, Z. A., Mallah, G. A., Piran, M. J., & Dharejo, F. A. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.10.026 · Cited by 14

Distributed Rumor Source Detection via Boosted Federated Learning

Wang, R., Zhang⋆, Y., Wan, W., Chen, M., & Guizani, M. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3390238 · Cited by 13

Quantifying Bytes: Understanding Practical Value of Data Assets in Federated Learning

Yao, M., Qi, S., Tian, Z., Li, Q., Han, Y., Li, H., & Qi, Y. (2024) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010034 · Cited by 13

AI-enabled routing in next generation networks: A survey

Aktas, F., Shayea, I. A. M., Ergen, M., Saoud, B., Yahya, A. E., & Aldasheva, L. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.01.095 · Cited by 21

Prediction and Privacy Scheme for Traffic Flow Estimation on the Highway Road Network

Akallouch, M., Akallouch, O., Fardousse, K., Bouhoute, A., & Berrada, I. (2022) · Information · doi:10.3390/info13080381 · Cited by 13

An Innovative Hashgraph-based Federated Learning Approach for Multi Domain 5G Network Protection

Kholidy, H. A., & Kamaludeen, R. (2022) · IEEE Future Networks World Forum (FNWF) · doi:10.1109/fnwf55208.2022.00033 · Cited by 14

Hierarchical federated learning with local model embedding

He, Y., Yan, D., & Chen, F. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106148 · Cited by 15

Gated Recurrent Unit Embedded with Dual Spatial Convolution for Long-Term Traffic Flow Prediction

Zhang, Q., Zhou, L., Su, Y., Xia, H., & Xu, B. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12090366 · Cited by 14

FedGRU: Privacy-preserving Traffic Flow Prediction via Federated Learning

Liu, Y., Zhang, S., Zhang, C., & Yu, J. J. Q. (2020) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc45102.2020.9294453 · Cited by 54

FediTT: A Federated Learning Approach for Privacy-Preserving Traffic Flow Prediction

Deng, J., Hao, Y., Wang, J., & Jin, Z. (2024) · International Conference on Universal Village (UV) · doi:10.1109/uv63228.2024.11189123

Road network traffic flow prediction: A personalized federated learning method based on client reputation

Dai, G., Tang, J., Zeng, J., Hu, C., & Zhao, C. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109678 · Cited by 21

Federated Learning-Based Framework for Privacy-Preserving Traffic Flow Prediction

M, A. K., & Salim, A. (2025) · International Conference on Data Science and Intelligent Network Computing (ICDSINC) · doi:10.1109/icdsinc66221.2025.11448200

Prediction and Privacy Scheme for Traffic Flow Estimation on the Highway Road Network

Akallouch, M., Akallouch, O., Fardousse, K., Bouhoute, A., & Berrada, I. (2022) · Information · doi:10.3390/info13080381 · Cited by 13