Edge Intelligence: Federated Learning-Based Privacy Protection Framework for Smart Healthcare Systems
Open accessAbstract
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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An edge aggregator that introduces artificial noise to client model parameters in three steps still provided good model accuracy while achieving privacy due to the introduction of the noise.
“Therefore, an edge aggregator is proposed that introduces artificial noise to the client model parameters in three steps [27]. The introduction of this aggregator still provided good model accuracy while achieving privacy due to the introduction of the noise.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [27]
Akter et al. proposed an edge aggregator that introduces noise to clients' model parameters and employs differential privacy while considering the privacy budget, and results showed high model accuracy.
“To achieve this goal, Akter et al. [27] proposed an edge aggregator that introduces noise to the clients’ model parameters and by employing DP while considering the privacy budget, the results showed high model accuracy.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [27]
Differential privacy provides an effective mechanism to safeguard the exchange of ML model parameters from clients to FL or edge server.
“The differential privacy approach provides an effective mechanism to safeguard the exchange of ML model parameters from clients to FL or edge server [27,28,29,31].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [27]
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A Survey on Privacy Attacks and Defenses in Graph Neural Networks
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- Authors
- Akter, M., Moustafa, N., Lynar, T., & Razzak, I.
- Affiliations
- UNSW Canberra (AU)
- UNSW Sydney (AU)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2022-12
- Volume
- 26
- Issue
- 12
- Pages
- 5805-5816
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- 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
- 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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