Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

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Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network

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Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications

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Fed-NTP: A Federated Learning Algorithm for Network Traffic Prediction in VANET

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SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence

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A Communication-Efficient Federated Learning Scheme for IoT-Based Traffic Forecasting

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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

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Artificial neural network‐driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

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Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting

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LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting

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Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems

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Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans

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Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction

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Federated learning with SARIMA-based clustering for carbon emission prediction

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Survey on Federated-Learning Approaches in Distributed Environment

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A solar forecasting framework based on federated learning and distributed computing

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The Role of Artificial Intelligence and Machine Learning in Advancing Civil Engineering: A Comprehensive Review

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A Multi-Layer Model Based on Transformer and Deep Learning for Traffic Flow Prediction

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Detecting Electrocardiogram Arrhythmia Empowered With Weighted Federated Learning

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Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile-Edge Computing Networks

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Li, C., & Liu, W. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3325936 · Cited by 37

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FedGODE: Secure traffic flow prediction based on federated learning and graph ordinary differential equation networks

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AdaFed: Optimizing Participation-Aware Federated Learning With Adaptive Aggregation Weights

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Adaptive Traffic Prediction at the ITS Edge With Online Models and Blockchain-Based Federated Learning

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Decentralized Federated Learning for Road User Classification in Enhanced V2X Networks

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FedGCN: A Federated Graph Convolutional Network for Privacy-Preserving Traffic Prediction

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