## 3 Fusion of FL and Medical Domain
### Challenges of FL in Medical Domain  
Although FL has many promising applications in the medical domain, at the same time, there are numerous challenges while applying this technology to real medical settings. These challenges can be associated with training data, clients (hospitals in medical cases), servers, aggregation algorithm (global model), network structure, training procedures, model and parameters, inference, convergence, social (privacy), and deployment. In this work, we focus mainly on data-related challenges as data are the cornerstone of AI developments. Figure 6 presents challenges/issues related to FL in medical and COVID-19. In Figure 6, we categorize the FL challenges/issues into two types: general medical domain (13 challenges) and COVID-19 (6 challenges). However, it is worth noting that both the \"general medical domains\" and the \"COVID-19 scenario\" mostly share the same issues/challenges when it comes to FL application, but the last six issues were commonly observed during the COVID-19 pandemic. For instance, the spatial data of diverse types were collected and processed with AI/FL models as a non-pharmaceutical intervention to curb the virus spread, which is unique to communicable diseases like COVID-19. In contrast, poisoned data and privacy issues are very common in both the \"general medical domains\" as well as the \"COVID-19 scenario\". In conclusion, most data-related challenges are common between both whereas there exist some challenges that solely apply to either \"general medical domains\" or \"COVID-19 scenario\". Figure 5: Categorization of FL medical applications. In the general medical domain, data heterogeneity across hospitals is the most pressing challenge because it can lead to deficient model performance and prolong convergence time. Also, the data can be non-IID, which is a serious challenge in FL-based systems because it is hard to distinguish whether the low performance of the AI model is due to non-IID data or poisoned data. In some cases, the data can be poisoned via label flip, feature drop, and/or wrong labels, which can lead to wrong results in local model training and disparity in the global model. The data at different hospitals can be low in quality (outliers, missing data, low-resolution images, noisy signals, etc. ), which can lead to poor convergence and low accuracy. In many medical applications, the data can be enclosed in different formats (e.g., different MRI manufacturers), posing a serious challenge to connecting dots or curating fused knowledge. In some cases, all data are not available at the training time as it can be pulled in real-time from wearable devices, so the local model can obtain different data in each phase, leading to inconsistent results. In some cases, it is hard to classify data based on disease as the characteristics of each disease are different, and analysis of diverse data about the same disease is very challenging. In some cases, data are not available in commodity for conducting disease analysis and obtaining more data is challenging due to privacy results. In some cases, the data can overlap across hospitals, and training AI models over the same data can lead to a waste of computing resources. Data valuation is also very hard as the data quality valuation metrics can vary across hospitals, which may lead to higher inconsistencies in data and can contribute to information loss. Privacy preservation of medical data is challenging in the FL setting because the sensitive properties of data can still be leaked in model parameter exchange. The unavailability of benchmark datasets for some diseases hinders the proper evaluation of AI models. There are no procedures and mechanisms to identify the underperforming subpopulations to rectify them during the training process because the data sit in silos. Lastly, the data at each site can be biased  
Figure 6: Challenges/issues of FL in the medical context. toward some major populations/communities, which can degrade the local and global model performance.