Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities

Hanyue Xu, Kah Phooi Seng (), Jeremy Smith and Li Minn Ang
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Hanyue Xu: School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215000, China
Kah Phooi Seng: School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215000, China
Jeremy Smith: Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK
Li Minn Ang: School of Science, Technology and Engineering, University of the Sunshine Coast, Petrie, QLD 4502, Australia

Future Internet, 2024, vol. 16, issue 3, 1-19

Abstract: In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the collaborative training of deep learning models within these systems encounters significant challenges, chiefly due to data privacy concerns and dealing with communication latency from large-scale IoT devices. To address these issues, multi-level split federated learning (multi-level SFL) has been proposed, merging the benefits of split learning (SL) and federated learning (FL). This framework introduces a novel multi-level aggregation architecture that reduces communication delays, enhances scalability, and addresses system and statistical heterogeneity inherent in large AIoT systems with non-IID data distributions. The architecture leverages the Message Queuing Telemetry Transport (MQTT) protocol to cluster IoT devices geographically and employs edge and fog computing layers for initial model parameter aggregation. Simulation experiments validate that the multi-level SFL outperforms traditional SFL by improving model accuracy and convergence speed in large-scale, non-IID environments. This paper delineates the proposed architecture, its workflow, and its advantages in enhancing the robustness and scalability of AIoT systems in smart cities while preserving data privacy.

Keywords: federated learning; split learning; split federated learning; artificial intelligent internet of things; edge computing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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