## 4 Potential Applications of FL Technology in the COVID-19 Era  
In this section, we present some promising applications of FL that have helped societies across the globe to lower the effects of COVID-19 via technology. Some promising applications of FL are virus detection from image/X-ray data, diagnosis based on heterogeneous source data fusion, and outcome prediction [28]. Furthermore, FL has also played a key role in understanding the dynamics of COVID-19 via institute information/model sharing of COVID-19 patient data with privacy preservation. Although work has been published related to COVID-19 detection, AI usage in the COVID-19 context, and generic AI applications, a holistic picture of FL applications from a broader perspective, has not been discussed in the published literature. In Figure 7, we cover this gap by presenting recently proposed/developed promising applications of FL in detail.  
Because the future of healthcare can benefit from FL to a great extent, many FL-related projects are underway across the globe to determine its potential for future endeavors. A remarkable initiative in this regard is the Trustworthy Federated Data Analytics (TFDA) [[https://tfda.hmsp.center/](https://tfda.hmsp.center/)]([https://tfda.hmsp.center/](https://tfda.hmsp.center/)) (accessed on 29 January 2024) project. TFDA adapts the core concept of FL (algorithms \(\rightarrow\) data) in a regulatory-compliant and trustworthy way, instead of using a data-centric approach (data \(\rightarrow\) algorithms). The TFDA project will be validated in medical research from both theoretical and technical perspectives before utilization on a wider scale. The Joint Imaging Platform (JIP) [[https://jip.dktk.dkfz.de/ijphomepage/](https://jip.dktk.dkfz.de/ijphomepage/)]([https://jip.dktk.dkfz.de/ijphomepage/](https://jip.dktk.dkfz.de/ijphomepage/)) (accessed on 31 January 2024) is another promising initiative of the German Cancer Consortium (DKTK), fostering image-based analysis without disclosing actual image data. This platform assists in performing image-related tasks with an easy-to-use toolkit developed solely based on the FL paradigm. In line with these works, Federated Tumor Segmentation (FeTS) [[https://www.med.upenn.edu/cbica/fets/](https://www.med.upenn.edu/cbica/fets/)]([https://www.med.upenn.edu/cbica/fets/](https://www.med.upenn.edu/cbica/fets/)) (accessed on 1 February 2024) is a third promising initiative for detecting a tumor boundary by leveraging diverse patient data without accessing private data. All these developments indicate the ever-increasing interest of researchers in the FL domain, especially in medical environments. Similarly, other developments [[https://cordis.europa.eu/project/id/826078/reporting](https://cordis.europa.eu/project/id/826078/reporting)]([https://cordis.europa.eu/project/id/826078/reporting](https://cordis.europa.eu/project/id/826078/reporting)) (accessed on 3 February 2024), [[https://owkin.com/connect](https://owkin.com/connect)]([https://owkin.com/connect](https://owkin.com/connect)) (accessed on 5 February 2024), are underway concerning COVID-19 in an attempt to eradicate this deadly pandemic from societies as soon as possible. The details of each FL application can be learned by searching for relevant studies using application names. The analysis demonstrated in Figure 7 can pave the way to learning the potential of FL in the medical domain, especially in the context of COVID-19. Apart from the main applications, in some cases, FL has been utilized to improve critical parts of ML algorithms. For example, some studies have demonstrated FL use as pre-processing and on-demand data provisioning in ML environments.