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Graph Split Federated Learning for Distributed Large-Scale AIoT in Smart Cities
Journal article   Open access   Peer reviewed

Graph Split Federated Learning for Distributed Large-Scale AIoT in Smart Cities

Hanyue Xu, K. P. Seng, L. M. Ang, W. Wang and J. Smith
IEEE Open Journal of the Computer Society, Vol.6, pp.1027-1040
2025
DOI: https://doi.org/10.1109/OJCS.2025.3583271
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Published Version (Advanced Access)CC BY V4.0,  Open Access

Abstract

artificial intelligence internet of things distributed collaborative machine learning graph convolution neural networks passenger demand forecasting Split federated learning
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 paper 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.

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