The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944481 · Cited by 1,725
Secure, privacy-preserving and federated machine learning in medical imaging
Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0186-1 · Cited by 1,676 · cited 1×
“Since 2015, the concept of federated learning has attracted much attention [27].”
BackgroundFederated Learning for Healthcare Informatics
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00082-4 · Cited by 1,542 · cited 1×
“It holds much promise in healthcare data analytics [28].”
BackgroundShafique, K., Khawaja, B. A., Sabir, F., Qazi, S., & Mustaqim, M. (2020) · IEEE Access · doi:10.1109/access.2020.2970118 · Cited by 1,401
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring
Wu, Q., Chen, X., Zhou, Z., & Zhang, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3045266 · Cited by 392
Lim, W. Y. B., Ng, J. S., Xiong, Z., Jin, J., Zhang, Y., Niyato, D. T., Leung, C., & Miao, C. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3096076 · Cited by 320
Privacy and Security Issues in Deep Learning: A Survey
Liu, X., Xie, L., Wang, Y., Zou, J., Xiong, J., Ying, Z., & Vasilakos, A. V. (2020) · IEEE Access · doi:10.1109/access.2020.3045078 · Cited by 311
VFL: A Verifiable Federated Learning With Privacy-Preserving for Big Data in Industrial IoT
Fu, A., Zhang, X., Xiong, N. N., Gao, Y., Wang, H., & Zhang, J. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3036166 · Cited by 297
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2022) · ACM Computing Surveys · doi:10.1145/3453158 · Cited by 295
Cited by
Sorted by citations
Zhou, X., Ye, X., Wang, K. I., Liang, W., Nair, N. C., Shimizu, S., Yan, Z., & Jin, Q. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3259431 · Cited by 235
Recent methodological advances in federated learning for healthcare
Zhang, F., Kreuter, D., Chen, Y., Dittmer, S., Tull, S., Shadbahr, T., Schut, M. C., Asselbergs, F. W., Kar, S., Sivapalaratnam, S., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101006 · Cited by 152
A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems
Gu, X., Sabrina, F., Fan, Z., & Sohail, S. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20156539 · Cited by 139
Fouda, M. M., Fadlullah, Z. M., Ibrahem, M. I., & Kato, N. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3486690 · Cited by 103
Yang, Z., Chen, Y., Huangfu, H., Ran, M., Wang, H., Li, X., & Zhang, Y. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3317632 · Cited by 88
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
