Cited by 134Privacy-Preserving Technologies in DataIoT and Edge/Fog ComputingBlockchain Technology Applications and Security

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Edge Intelligence: Federated Learning-Based Privacy Protection Framework for Smart Healthcare Systems

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Akter, Mahmuda, Moustafa, Nour, Lynar, Timothy, Razzak, Imran·2022·IEEE Journal of Biomedical and Health Informatics

DOI 10.1109/jbhi.2022.3192648

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Abstract

Federated learning methods offer secured monitor services and privacy-preserving paradigms to end-users and organisations in the Internet of Things networks such as smart healthcare systems. Federated learning has been coined to safeguard sensitive data, and its global aggregation is often based on a centralised server. This design is vulnerable to malicious attacks and could be breached by privacy attacks such as inference and free-riding, leading to inefficient training models. Besides, uploaded analysing parameters by patients can reveal private information and the threat of direct manipulation by the central server. To address these issues, we present a three-fold Federated Edge Aggregator, the so-called Edge Intelligence, a federated learning-based privacy protection framework for safeguarding Smart Healthcare Systems at the edge against such privacy attacks. We employ an iteration-based Conventional Neural Network (CNN) model and artificial noise functions to balance privacy protection and model performance. A theoretical convergence bound of Edge Intelligence on the trained federated learning model's loss function is also introduced here. We evaluate and compare the proposed framework with the recently established methods using model performance and privacy budget on popular and recent datasets: MNIST, CIFAR10, STL10, and COVID19 chest x-ray. Finally, the proposed framework achieves 90% accuracy and a high privacy rate demonstrating better performance than the baseline technique.

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Authors: Akter, M., Moustafa, N., Lynar, T., & Razzak, I.
Affiliations: UNSW Canberra (AU)
UNSW Sydney (AU)
ORCID: Akter, Mahmuda
Moustafa, Nour
Lynar, Timothy
Razzak, Imran
Venue: IEEE Journal of Biomedical and Health Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-12
Volume: 26
Issue: 12
Pages: 5805-5816
ISSN: 2168-2194 (ISSN-L)
2168-2208
DOI: 10.1109/jbhi.2022.3192648
OpenAlex: W4286210752
Semantic Scholar: 53945286351127feab2bdfcb5ca6312633764db8
Cited by: 128
References: 42
Citations by year: 2026: 31 · 2025: 54 · 2024: 33 · 2023: 10
FWCI: 7.98
Type: journal article
Language: en
Open access: green
figshare.com
Licence: cc-by
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Journal of Biomedical and Health Informatics (publishedVersion)
PubMed (publishedVersion)
Figshare (submittedVersion, cc-by)
Funders: University of New South Wales Canberra
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
Keywords: federated learning, edge AI, privacy preservation, smart healthcare, inference attacks, free-riding attacks, artificial noise, convergence bounds, convolutional neural networks, privacy-utility trade-off, edge aggregation, chest X-ray classification
MeSH terms: COVID-19, Fenbendazole, Humans, Intelligence, Privacy, Internet

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Edge Intelligence: Federated Learning-Based Privacy Protection Framework for Smart Healthcare Systems

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