Privacy-Preserving Federated Learning Model for Healthcare Data
Open accessAbstract
Federated Machine Learning (FL) can be used effectively in distributed datasets, where data owners hesitate to share their raw data, as a reliable approach to train an ML algorithm. However, in the case of sensitive healthcare datasets, additional privacy measures before feeding into machine learning mechanisms are also necessary. Our approach uses the federated learning framework, which removes the necessity of sharing patients' sensitive data in a raw format outside the premise. First, the data owners agree on a list of features selected by the correlation; then, after training the local models, the obtained local models are transmitted to the central server for aggregation. The differential privacy (DP) approach is adopted to perturb the local models before transmission to add an extra privacy layer. As a result, our framework achieves improved utility as the feature selection reduces the data dimension. Finally, based on the patient's genomic data, the framework establishes a practical healthcare application to privacy-predict certain heart failure/cancer diseases. application to predict certain heart failure diseases in a private manner.
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Statistical feature selection and differential privacy approaches reduce data transmission and add noise to local models before sending them to the federated learning server, and results showed good accuracy with the desired level of privacy.
“Statistical feature selection and differential privacy approaches are adopted [28] to reduce the data transmission and to add noise to the local models before sending those to the FL server for aggregation. The results showed good accuracy with the desired level of privacy applied to the model parameters.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [28]
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 [28]
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References 33 · Cited by 56
Research works printed but not linked: 25
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- Authors
- Islam, T. U., Ghasemi, R., & Mohammed, N.
- Affiliations
- University of Manitoba (CA)
- Bu-Ali Sina University (IR)
- Venue
- 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)
- Publisher
- IEEE
- Published
- 2022-01-26
- Pages
- 0281-0287
- OpenAlex
- W4214940550
- Cited by
- 56
- References
- 33
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 18 · 2023: 11 · 2022: 5
- FWCI
- 6.91
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Annual Computing and Communication Workshop and Conference (CCWC) (publishedVersion)
- Mspace (University of Manitoba) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- privacy-preserving federated learning, healthcare data privacy, differential privacy, feature selection, model aggregation, genomic data, heart failure prediction, cancer prediction, sensitive data sharing
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