Smart City Gnosys

Smart city article details

Title	Graph Split Federated Learning For Distributed Large-Scale Aiot In Smart Cities
ID_Doc	28274
Authors	Xu H.; Seng K.P.; Ang L.-M.; Wang W.; Smith J.
Year	2025
Published	IEEE Open Journal of the Computer Society, 6
DOI	http://dx.doi.org/10.1109/OJCS.2025.3583271
Abstract	The rise of smart cities has leveraged the power of Internet of Things devices to transform urban services. A key element of this transformation is the widespread deployment of IoT devices for data collection, which feeds into machine learning algorithms to improve city services. However, the centralization of sensitive IoT data for ML raises privacy and efficiency concerns. Distributed collaborative machine learning, particularly split federated learning, has emerged as a solution, enabling privacy-preserving, resource-efficient training on IoT devices. This article introduces a novel SFL-based framework for graph convolutional neural networks, SFLGCN, which includes two variants SFLGCN (general) and SFLGCN-PP (Privacy Preservation), specifically designed for resource-constrained IoT systems in smart cities. SFLGCN-PP, an enhanced version of the framework, focuses on privacy preservation and is capable of handling graph-structured data, which is common in smart city scenarios, without requiring pre-defined adjacency matrices, thus enhancing data privacy. The framework’s efficacy is validated through predictive modeling of autonomous vehicle passenger demand using real-world IoT data. Additionally, the generalization capability of our framework is demonstrated on public graph datasets, where it outperforms traditional federated learning in graph neural network tasks, particularly in large-scale IoT environments with varying data distributions and client capacities. © 2020 IEEE.
Author Keywords	artificial intelligence internet of things; distributed collaborative machine learning; graph convolution neural networks; passenger demand forecasting; Split federated learning

Similar Articles

Id	Similarity	Authors	Title	Published
38262	0.895	Xu H.; Seng K.P.; Smith J.; Ang L.M.	Multi-Level Split Federated Learning For Large-Scale Aiot System Based On Smart Cities	Future Internet, 16, 3 (2024)
54221	0.888	Yang H.; Liu H.; Yuan X.; Wu K.; Ni W.; Zhang J.A.; Liu R.P.	Synergizing Intelligence And Privacy: A Review Of Integrating Internet Of Things, Large Language Models, And Federated Learning In Advanced Networked Systems	Applied Sciences (Switzerland), 15, 12 (2025)
52772	0.884	Lokesh G.H.; Hukkeri G.S.; Jhanjhi N.Z.; Lin H.	Split Federated Learning For Secure Iot Applications: Concepts, Frameworks, Applications And Case Studies	Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies (2024)
5461	0.884	Zhang J.; Tal I.	A Systematic Review Of Contemporary Applications Of Privacy-Aware Graph Neural Networks In Smart Cities	ACM International Conference Proceeding Series (2024)
26377	0.882	Li W.; Wang S.	Federated Meta-Learning For Spatial-Temporal Prediction	Neural Computing and Applications, 34, 13 (2022)
26321	0.881	Djenouri Y.; Michalak T.P.; Lin J.C.-W.	Federated Deep Learning For Smart City Edge-Based Applications	Future Generation Computer Systems, 147 (2023)
26359	0.879	Gandhi M.; Singh S.K.; Ravikumar R.N.; Vaghela K.	Federated Learning In Secure Smart City Sensing: Challenges And Opportunities	Edge of Intelligence: Exploring the Frontiers of AI at the Edge (2025)
42595	0.873	Soares K.; Shinde A.A.; Patil M.	Ppfedsl: Privacy Preserving Split And Federated Learning Enabled Secure Data Sharing Model For Internet Of Vehicles In Smart City	International Journal of Computer Networks and Applications, 12, 2 (2025)
43157	0.872	Abdel-Basset M.; Hawash H.; Moustafa N.; Razzak I.; Abd Elfattah M.	Privacy-Preserved Learning From Non-I.I.D Data In Fog-Assisted Iot: A Federated Learning Approach	Digital Communications and Networks, 10, 2 (2024)
5652	0.872	Wang S.; Chen C.; Han B.; Zhu J.	A Trusted And Decentralized Federated Learning Framework For Iot Devices In Smart City	Proceedings - IEEE Congress on Cybermatics: 2024 IEEE International Conferences on Internet of Things, iThings 2024, IEEE Green Computing and Communications, GreenCom 2024, IEEE Cyber, Physical and Social Computing, CPSCom 2024, IEEE Smart Data, SmartData 2024 (2024)