Federated Learning for Healthcare: A Comprehensive Review
Open accessAbstract
Recent advancements in deep learning for healthcare and computer-aided laboratory services have sparked a renewed interest in making medical data more accessible. Elevating the quality of healthcare services and delivering improved patient care necessitates a knowledge base rooted in data-driven insights. Deep learning models have proven to excel in this regard, as they are specifically designed to embrace a data-driven approach. These models thrive on exposure to larger datasets, which enables them to continuously improve their performance. However, as healthcare organizations strive to aggregate clinical records onto central servers to construct robust deep learning models, concerns surrounding privacy, data ownership, and legal restrictions have emerged. Safeguarding sensitive medical data while harnessing collective knowledge from multiple healthcare centers is a challenging balancing act. One promising approach to address these concerns is the use of privacy-preserving techniques that allow for the utilization of data from multiple centers without compromising security. Federated learning (FL) is a technique that has emerged to enable the deployment of large machine learning models trained across multiple data centers without the necessity of sharing sensitive information. In this article, we present the most recent findings derived from a systematic literature review focusing on the application of federated learning in healthcare settings. This review offers insights into the current state of research and practical implementations of FL within the healthcare domain. By leveraging federated learning, healthcare institutions can harness the collective power of their data while upholding privacy and data security standards, ultimately leading to more effective and data-driven healthcare solutions.
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In a multi-hospital diabetes research, federated learning decreased the risk of data breaches by 40% while also improving predicted outcomes by 15% in electronic health record analysis as compared to centralized machine learning.
“For example, in a multi-hospital managing diabetes research, FL decreased the risk of data breaches by 40% while also improving predicted outcomes by 15% in EHR analysis as compared to centralized ML [4,34].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [34]
Section 1.2 (page unavailable) says hospitals can train federated models without transmitting patient data and that this lessens security concerns. The copy does not report a multi-hospital diabetes study, a 40% reduction in breach risk, or a 15% improvement in EHR predictions compared with centralized ML. (LLM, conf=0.88)
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- Authors
- Dhade, P., & Shirke, P.
- Affiliations
- Savitribai Phule Pune University (IN)
- Department of Computer Science & Engineering, Sandip University, Nashik 422213, Maharashtra, India
- Corresponding author
- Dhade, Pallavi
- Venue
- RAiSE-2023
- Publisher
- MDPI
- Published
- 2024-02-09
- Pages
- 230
- OpenAlex
- W4391748641
- Cited by
- 67
- References
- 29
- Citations by year
- 2026: 15 · 2025: 39 · 2024: 13
- FWCI
- 19.62
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Conference on Recent Advances in Science and Engineering (RAiSE) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- federated learning, health care, privacy-preserving machine learning, medical data security, data ownership, data-driven healthcare
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