Federated deep learning systems in healthcare
Book chapter
Tanjil, Ashraful Reza, Bhuiyan, Fahim Mohammad Adud, Rony, Mohammad Abu Tareq and Biswas, Kamanashis. (2024). Federated deep learning systems in healthcare. In In Kaur, Amandeep, Kaushal, Chetna, Hassan, Md. Mehedi and Aung, Si Thu (Ed.). Federated deep learning for healthcare : A practical guide with challenges and opportunities pp. 31-49 CRC Press. https://doi.org/10.1201/9781032694870-3
Authors | Tanjil, Ashraful Reza, Bhuiyan, Fahim Mohammad Adud, Rony, Mohammad Abu Tareq and Biswas, Kamanashis |
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Editors | Kaur, Amandeep, Kaushal, Chetna, Hassan, Md. Mehedi and Aung, Si Thu |
Abstract | Electronic Medical Records (EMRs) have transformed healthcare data, and federated learning (FL) stands out as a ground-breaking strategy. As changes are incorporated into a global model, FL enables collaborative training of local models on privacy-sensitive EMRs. This procedure maintains patient data privacy while improving risk assessment, treatment plans, and diagnostics. FL is notable for fostering medical research while avoiding data centralization, which results in better healthcare insights. Managing diverse data, guaranteeing security, and eliminating biases are still tricky. FL uses local model training on patient-specific data, followed by collaborative updates. This method preserves patient privacy by keeping raw data in regional models. Security is further strengthened by encryption methods that protect privacy. The inherent heterogeneity spanning devices, data kinds, and model structures must be considered while developing FL models. Scalability and efficiency must be considered while using FL in the healthcare industry. Healthcare datasets are large, diverse, and sensitive, highlighting the need for efficient approaches. Data partitioning, model architecture, preprocessing, communication optimization, and resource allocation are all crucial for a successful deployment. FL can deliver breakthrough healthcare insights while protecting patient privacy by taking these factors. |
Page range | 31-49 |
Year | 2024 |
Book title | Federated deep learning for healthcare : A practical guide with challenges and opportunities |
Publisher | CRC Press |
Place of publication | Boca Raton, Florida |
London, United Kingdom | |
New York, New York | |
Series | Advances in smart healthcare technologies |
ISBN | 9781032689555 |
9781032689555 | |
9781032694870 | |
Digital Object Identifier (DOI) | https://doi.org/10.1201/9781032694870-3 |
Scopus EID | 2-s2.0-85202748366 |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 02 Oct 2024 |
2024 | |
Publication process dates | |
Deposited | 28 May 2025 |
Additional information | © 2025 selection and editorial matter, Amandeep Kaur, Chetna Kaushal, Md. Mehedi Hassan, and Si Thu Aung; individual chapters, the contributors. |
https://acuresearchbank.acu.edu.au/item/91x94/federated-deep-learning-systems-in-healthcare
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