BIO Web Conf.
Volume 86, 2024International Conference on Recent Trends in Biomedical Sciences (RTBS-2023)
|Number of page(s)
|12 January 2024
Federated Learning for Predictive Healthcare Analytics: From theory to real world applications
1 School of Engineering Design and Automation, GNA University, Phagwara, Punjab, India
2 School of Computational Science, GNA University, Phagwara, Punjab, India
* Corresponding author: email@example.com
In the contemporary landscape, machine learning has a pervasive impact across virtually all industries. However, the success of these systems hinges on the accessibility of training data. In today's world, every device generates data, which can serve as the building blocks for future technologies. Conventional machine learning methods rely on centralized data for training, but the availability of sufficient and valid data is often hindered by privacy concerns. Data privacy is the main concern while developing a healthcare system. One of the technique which allow decentralized learning is Federated Learning. Researchers have been actively applying this approach in various domains and have received a positive response. This paper underscores the significance of employing Federated Learning in the healthcare sector, emphasizing the wealth of data present in hospitals and electronic health records that could be used to train medical systems.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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