## 7 Concluding Remarks and Future Work  
This article presented an overview of the FL paradigm in the context of the general medical domain and COVID-19. Specifically, we highlighted the need for FL in the medical domain, the architecture of FL in the medical domain, promising applications of FL in the medical domain including COVID-19, the integration of FL with emerging technologies to improve various service scenarios of FL and its privacy, and the technical challenges that practitioners face in FL design/deployment in the medical domain. We demonstrated the evolution of the AI domain (i.e., local learning \(\rightarrow\) swarm learning), and the resulting technologies of FL (federated analytics and swarm learning). We discuss the major taxonomies of the FL systems in six aspects and enlist technical challenges in the FL ecosystem that are the foundation of many adversarial attacks on these systems. Finally, some key research areas that need further exploration from the AI community are suggested to increase the reliability of FL in the medical domain. This study's findings can pave the way to discern the progress FL has made so far in the medical field, and the practical issues stemming from FL application in this line of work. In recent years, FL systems have widely contributed to training AI models of higher generalizability, which can assist doctors in many ways such as disease classification, disease prediction, prescribing medications, and severity assessment of various kinds of diseases. The FL has abilities to produce robust AI models by using diverse and large-scale datasets held by many hospitals (or medical institutes), and such AI models are in dire need in the medical domain considering the rapid rise in clinical complications of many diseases. Since the medical/healthcare domain belongs to safety-critical applications, and therefore, FL has huge prospects in it in terms of preventing wrong medication, prescriptions, and treatment procedures compared to CL by exposing underlying AI models to diverse datasets. FL can widely contribute to early and accurate diagnosis of some critical diseases such as Alzheimer's, autism, lung cancer, etc. In addition, FL can foster collaboration across medical institutes while alleviating the stringent barrier of privacy which was not possible in the recent past (e.g., before the year 2016). It can also contribute to processing data of diverse modalities and identifying the critical parts in medical images, leading to faster and more reliable diagnosis with significantly reduced cost. Based on the above analysis, it is fair to say that FL has revolutionized the medical domain and it can contribute to developing data-driven healthcare products/solutions. In the recent pandemic, FL has also contributed to sharing medical knowledge across the institute to better plan treatment procedures. It is worth noting that FL is not a silver bullet, and there exist many technical issues in it when it comes to real-world deployments. For example, due to its distributed nature, it is prone to adversarial attacks of diverse types (e.g., backdoor, data poisoning, model poisoning, etc.). It cannot prevent privacy risks of all types, and personal information or data characteristics are still leaked when clients communicate with the server. Also, the convergence of the global model takes a long time, when either most clients do not share their updates or data at some clients is non-IID. Hence, more efforts from different stakeholders are needed to overcome these issues and to make this technology beneficial for people around the globe. In future work, we intend to explore the role/potential of two emerging technologies (i.e., federated analytics and swarm learning) that resulted from FL in the medical context. We also plan to investigate the applications and challenges of serverless FL in the financial sector, which is a promising research topic in the AI-driven era. All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.