Leveraging towards Privacy-preserving using Federated Machine Learning for Healthcare Systems
Abstract
With the advent of artificial intelligence, healthcare industry has been successful in improving medical diagnosis, medical prognosis, and other clinical tasks using machine learning. The success of improving healthcare services is largely due to the amount of patients data acquired usually in privacy-invasive ways for the process of machine learning. The issue of privacy-preserving and utilizing the large and diverse private datasets is solved using federated learning. It can perform predictive tasks alongside maintaining privacy of medical data. In this paper, we have proposed cancer text classification system using federated learning to maintain the integrity of sensitive medical data. The acquisition of data from various healthcare institutions is used for detection of cancer in preliminary stage in the patient's body. The experiment has been conducted for various types of healthcare environmental settings for the process of federated learning including change in the number of health institutions involved, change in data distribution among numerous healthcare institutions involved, and performance in case of applying various machine learning models. Here, we utilize several machine learning models namely, Recurrent Neural Network (RNN), Bi-RNN, Gated Recurrent Unit (GRU), and Long short-term Memory (LSTM) for text classification that attains accuracy of 57%, 59%, 60%, and 61% respectively. The paper proposes a novel federated learning based system capable of text classification in the healthcare industry with the use of federated machine learning alongside maintaining data privacy.
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A cancer text classification system based on federated learning uses homomorphic encryption to secure model parameter exchange between federated learning participants and server.
“Similarly, a cancer text classification system based on FL is proposed that uses homomorphic encryption to secure model parameter exchange between FL participants and server [35].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [35]
Gandhi et al. proposed a federated learning system to maintain integrity of sensitive medical data using deep-learning models for cancer text classification, and results showed effectiveness in addressing privacy issues.
“Gandhi et al. [35] proposed an FL system to maintain the integrity of sensitive medical data by using a few deep-learning models for cancer text classification. Their results showed that the proposed framework is effective in addressing the privacy issues of sensitive medical data for cancer text classification.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [35]
HE is very effective for FL privacy preservation because most operations required by FL systems, such as aggregation, model updates, and gradient calculations, can be performed without accessing raw data.
“This approach is very effective for FL privacy preservation as most of the operations required by the FL systems like aggregation, model updates, and gradient calculations can be performed without accessing the raw data [34,35,36,37,38].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [35]
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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 · see citing works
- Authors
- Gandhi, N., Mishra, S., Bharti, S. K., & Bhagat, K.
- Affiliations
- Pandit Deendayal Energy University (IN)
- Google (United States) (US)
- Venue
- 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)
- Publisher
- IEEE
- Published
- 2021-07-09
- Pages
- 1-6
- OpenAlex
- W4205528897
- Cited by
- 11
- References
- 24
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 3
- FWCI
- 0.69
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- federated learning, privacy-preserving machine learning, healthcare data privacy, clinical text classification, data distribution skew, recurrent neural networks, gated recurrent unit, long short-term memory, early cancer detection
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