Smart City Gnosys

Smart city article details

Title	Fedstn: Graph Representation Driven Federated Learning For Edge Computing Enabled Urban Traffic Flow Prediction
ID_Doc	26403
Authors	Yuan X.; Chen J.; Yang J.; Zhang N.; Yang T.; Han T.; Taherkordi A.
Year	2023
Published	IEEE Transactions on Intelligent Transportation Systems, 24, 8
DOI	http://dx.doi.org/10.1109/TITS.2022.3157056
Abstract	Predicting traffic flow plays an important role in reducing traffic congestion and improving transportation efficiency for smart cities. Traffic Flow Prediction (TFP) in the smart city requires efficient models, highly reliable networks, and data privacy. As traffic data, traffic trajectory can be transformed into a graph representation, so as to mine the spatio-temporal information of the graph for TFP. However, most existing work adopt a central training mode where the privacy problem brought by the distributed traffic data is not considered. In this paper, we propose a Federated Deep Learning based on the Spatial-Temporal Long and Short-Term Networks (FedSTN) algorithm to predict traffic flow by utilizing observed historical traffic data. In FedSTN, each local TFP model deployed in an edge computing server includes three main components, namely Recurrent Long-term Capture Network (RLCN) module, Attentive Mechanism Federated Network (AMFN) module, and Semantic Capture Network (SCN) module. RLCN can capture the long-term spatial-temporal information in each area. AMFN shares short-term spatio-temporal hidden information when it trains its local TFP model by the additive homomorphic encryption approach based on Vertical Federated Learning (VFL). We employ SCN to capture semantic features such as irregular non-Euclidean connections and Point of Interest (POI). Compared with existing baselines, several simulations are conducted on practical data sets and the results prove the effectiveness of our algorithm. © 2000-2011 IEEE.
Author Keywords	deep learning; graph representation; smart city; Traffic flow prediction; vertical federated learning

Similar Articles

Id	Similarity	Authors	Title	Published
5610	0.929	Pulligilla M.K.; Vanmathi C.	A Traffic Flow Prediction Framework Based On Integrated Federated Learning And Recurrent Long Short-Term Networks	Peer-to-Peer Networking and Applications, 17, 6 (2024)
26321	0.92	Djenouri Y.; Michalak T.P.; Lin J.C.-W.	Federated Deep Learning For Smart City Edge-Based Applications	Future Generation Computer Systems, 147 (2023)
26393	0.913	Al-Huthaifi R.; Li T.; Al-Huda Z.; Huang W.; Luo Z.; Xie P.	Fedgode: Secure Traffic Flow Prediction Based On Federated Learning And Graph Ordinary Differential Equation Networks	Knowledge-Based Systems, 299 (2024)
24076	0.908	Jiang Z.	Enhancing Urban Traffic Forecasting With Scalable, Privacy-Preserving Federated Learning Approach	2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024 (2024)
23509	0.904	Ali A.; Ullah I.; Singh S.K.; Sharafian A.; Jiang W.; I. Sherazi H.; Bai X.	Energy-Efficient Resource Allocation For Urban Traffic Flow Prediction In Edge-Cloud Computing	International Journal of Intelligent Systems, 2025, 1 (2025)
53044	0.901	Meng X.; Xie W.; Cui J.	Stmgfn: Spatio-Temporal Multi-Graph Fusion Network For Traffic Flow Prediction	Lecture Notes in Computer Science, 15291 LNCS (2025)
44733	0.901	Karthick Raghunath K.M.; Rohith Bhat C.; Vinoth Kumar V.; Athiyoor Kannan V.; Mahesh T.R.; Manikandan K.; Krishnamoorthy N.	Redefining Urban Traffic Dynamics With Tcn-Fl Driven Traffic Prediction And Control Strategies	IEEE Access, 12 (2024)
60735	0.9	Kumari M.; Ulmas Z.; Suseendra R.; Ramesh J.V.N.; El-Ebiary Y.A.B., Prof.	Utilizing Federated Learning For Enhanced Real-Time Traffic Prediction In Smart Urban Environments	International Journal of Advanced Computer Science and Applications, 15, 2 (2024)
58593	0.898	Wang X.; Ma Y.; Wang Y.; Jin W.; Wang X.; Tang J.; Jia C.; Yu J.	Traffic Flow Prediction Via Spatial Temporal Graph Neural Network	The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (2020)
19912	0.895	Gupta S.; Torra V.	Differentially Private Traffic Flow Prediction Using Transformers: A Federated Approach	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14398 LNCS (2024)