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

Hanyue Xu; Kah Phooi Seng; Jeremy Smith; Li Minn Ang

doi:10.3390/fi16030082

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

Journal article

Open access

Peer reviewed

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

Future Internet, Vol.16(3), pp.1-19

2024

DOI: https://doi.org/10.3390/fi16030082

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Abstract

federated learning

split learning

split federated learning

artificial intelligent internet of things

edge computing

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.

Details

Title: Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities
Authors: Hanyue Xu - Xi’an Jiaotong-Liverpool University
Kah Phooi Seng (Corresponding Author) - Xi’an Jiaotong-Liverpool University
Jeremy Smith - University of Liverpool
Li Minn Ang - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: Future Internet, Vol.16(3), pp.1-19
Publisher: MDPI AG
Date published: 2024
DOI: 10.3390/fi16030082
ISSN: 1999-5903
Copyright note: Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Data Availability: The Fashion MNIST dataset can be found in Kaggle: https://www. kaggle.com/datasets/zalando-research/fashionmnist (accessed on 15 January 2024). The HAM10000 dataset comes from the paper: “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions”, DOI: https://doi.org/10.1038/sdata.2018.161.
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 991011698702621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Computer Science, Information Systems