A comprehensive review of federated learning for COVID‐19 detection. Issue 3 (6th December 2021)

• Record Type: Journal Article
• Authors: Naz, Sadaf; Phan, Khoa T.; Chen, Yi‐Ping Phoebe
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 3(2022)
• Page Start: 2371
• Page End: 2392
• Publication Date: 2021-12-06
• Subjects: COVID‐19 detection -- deep learning -- federated learning -- machine learning -- privacy preservation; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22777
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The coronavirus of 2019 (COVID‐19) was declared a global pandemic by World Health Organization in March 2020. Effective testing is crucial to slow the spread of the pandemic. Artificial intelligence and machine learning techniques can help COVID‐19 detection using various clinical symptom data. While deep learning (DL) approach requiring centralized data is susceptible to a high risk of data privacy breaches, federated learning (FL) approach resting on decentralized data can preserve data privacy, a critical factor in the health domain. This paper reviews recent advances in applying DL and FL techniques for COVID‐19 detection with a focus on the latter. A model FL implementation use case in health systems with a COVID‐19 detection using chest X‐ray image data sets is studied. We have also reviewed applications of previously published FL experiments for COVID‐19 research to demonstrate the applicability of FL in tackling health research issues. Last, several challenges in FL implementation in the healthcare domain are discussed in terms of potential future work.