## 5 FL Synergy with Other Digital Technologies in the Medical Context
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Paradigm** & **Synergy with** & **Purpose of Synergy** \\ \hline DP & Privacy protection of sensitive medical data \\ \hline Local DP & Privacy protection of FL parameters/gradients \\ \hline HE & Privacy preservation of data/parameter in transfer \\ \hline Blockchain & Privacy infrastructure to share COVID-19 information [35], resource allocation, self-testing, EWS, poisoning detection [36], etc. \\ \hline Edge CC & Secure analytics of COVID-19 image data \\ \hline Multilayer perceptron & Robust mortality predictive models \\ \hline Support vector machine (SVM) & Prediction of daily COVID-19 cases \\ \hline IoT & COVID-19 screening, medical data acquisition and efficient analysis [37] \\ \hline IoT big data & Fast detection of virus, Enhancing data quality [38] \\ \hline
5G-enabled architecture & Classifying severity of COVID-19 \\ \hline IIoT & Accurate detection of COVID-19, personalized healthcare [39] \\ \hline RL & Identifying COVID-19 from medical images, image segmentation [40], decision making [41] \\ \hline NAS & Face mask detection \\ \hline CC & Diagnosis [42], analytics of medical data [43], disease monitoring [44], QoS enhancement of FL-based systems [45] \\ \hline IoMT & Medical DSS for tracking COVID-19, collaboration among medical institutes [46] \\ \hline Transfer learning & Classifying COVID-19 from lung scans, breast cancer classification [47] \\ \hline Access control & To safeguard medical data in BDE \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of FL integration with digital techniques/technologies from medical context. In the past few years, the FL concept has significantly evolved. In Figure 8, we demonstrate the evolution of FL from local learning to federated analytics. The general AI concept started with local learning (LL). In LL, data and computations are usually in isolated sites, as seen in Figure 8a. Later, LL evolved into central learning where data moved to central locations to perform computations (also known as cloud-based ML). See Figure 8b. Later, CL evolved into FL, in which data are kept with their contributors, and computing is performed on the client side, but parameter/updates are orchestrated by the central server. See Figure 8c. Later, FL evolved into swarm learning (SL) in which there is no need for a central server because both data and parameters stay at the edge, as seen in Figure 8d. SL is one of the technologies resulting from FL. In 2017, Google introduced the concept of federated analytics (FA). In FA, the analytics can be performed on local devices with local data akin to FL. See Figure 8e. The FA concept was derived from the FL and belongs to the category of distributed computing paradigm [57]. Soon, FA will become one of the key technologies in the domain of collaborative learning. In the COVID-19 era, FA has been used for conducting analytics of COVID-19's impact and other diseases, analysis of vaccine efficacy on different subgroups, the categorization of COVID-19's effects based on demographics, for identification of risk factors related to COVID-19, forecasting vulnerability indexes, mortality/case predictions, forecasting disease trends, laboratory data analytics (cough samples, breathing patterns, oxygen levels, infected regions of lungs, etc. ), fMRI analysis, telemedicine, vulnerable group identification, candidate vaccine developments, and protein sequence analysis [58; 59; 60; 61].