###### Abstract  
Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms that can effectively work with decentralized data sources (e.g., hospitals) without acquiring any private data. Recently, applications of FL have vastly expanded into multiple domains, particularly the medical domain, and FL is becoming one of the mainstream technologies of the near future. In this study, we provide insights into FL fundamental concepts (e.g., the difference from centralized learning, functions of clients and servers, workflows, and nature of data), architecture and applications in the general medical domain, synergies with emerging technologies, key challenges (medical domain), and potential research prospects. We discuss major taxonomies of the FL systems and enlist technical factors in the FL ecosystem that are the foundation of many adversarial attacks on these systems. We also highlight the promising applications of FL in the medical domain by taking the recent COVID-19 pandemic as an application use case. We highlight potential research and development trajectories to further enhance the persuasiveness of this emerging paradigm from the technical point of view. We aim to concisely present the progress of FL up to the present in the medical domain including COVID-19 and to suggest future research trajectories in this area. federated learning; privacy preservation; AI models; training data; data sharing; medical domain; COVID-19; data sources; adversarial attacks; privacy-preserving paradigms; private data +
Footnote †: journal: Medical Domain  
ones. Modern healthcare systems are generating a mammoth amount of data that can unlock many financial opportunities for diverse stakeholders (e.g., drug makers, insurance providers, doctors, etc.) in medical science [6]. However, the CL-based approaches cannot be employed to unlock the potential of medical data as there is a risk of data manipulation or misuse [7]. To this end, the FL is crucial as it can resolve one of the critical challenges of privacy preservation while at the same time developing many data-driven products utilizing diverse hospital data [8]. In the recent past, FL has also played a vital role in addressing many aspects (e.g., testing, drug discovery, severity estimation, etc.) concerning the COVID-19 pandemic. FL is reshaping the healthcare industry with many innovative use cases that were not possible in the past due to restricted data access and regulations. Since FL is a privacy-by-design approach, it stays legally compliant because data do not leave the hospitals (or client devices). Furthermore, a powerful model can be trained collaboratively in the FL setting, and therefore, the data island problem can be solved effectively [9]. FL use in healthcare is highly beneficial because data often sit on an island, distributed massively, highly imbalanced, and non-IID (non-independent and identically distributed) in nature. Similarly, by applying FL in the COVID-19 era, detailed insights were obtained on this disease's progression, spread, and dynamics, which can vary from site to site. Although FL has shown remarkable improvements in addressing the data island, privacy, and ML model performance issues, it can be subjected to various attacks due to its distributed nature [10]. Commonly executed attacks on FL environments are data and model poisoning, the backdoor attack, gradient inversion, and class/feature leakage [11]. Due to the distributed nature of FL, many such attacks can go undetected, and many efforts are underway to secure the FL pipeline from attack. In this article, we provide a brief overview of the FL paradigm and its potential applications in the medical field including the COVID-19 pandemic. Then, we demonstrate the recent synergies/integration of the FL paradigm with other latest technologies (differential privacy, homomorphic encryption, blockchain, internet of things, ChatGPT, etc.) in order to either secure FL or extend the horizons of FL applications into multiple domains.