## 6 Future Research Trajectories for FL Ecosystem  
Since FL's inception, a substantial number of technical deficiencies in the FL ecosystem have been observed from multiple perspectives, and many developments are underway to enhance the persuasiveness of the FL ecosystem. For example, in the FL ecosystem, attackers can compromise clients' data or updates, corrupt the server-side global model, infer/reconstruct training data, slow the convergence speed, degrade the accuracy of global/local models, and/or predict sensitive information about clients [62]. In COVID-19 scenarios, the FL ecosystem can be trapped into slow convergence as well as lower prediction differences due to greater differences in data distributions/imbalances at each site [63]. Furthermore, deployment of the FL ecosystem for COVID-19 classification/prediction tasks can be subjected to adversarial attacks and data breaches. Hence, securing the FL ecosystem from a variety of adversarial attacks is an important concern in FL ecosystems [23]. In Figure 9, we present a detailed overview of the technical factors in the FL ecosystem that are the foundation of many adversarial attacks on these systems. The detailed analysis in Figure 9 can pave the way to improving the FL ecosystem from a technical perspective. In future endeavors, efficient solutions for all the challenges in Figure 9 are imperative in order to truly benefit from the potential of FL. Apart from the challenges in Figure 9, below, we pinpointed eight main future research aspects that need urgent solutions in the context of general FL and general medical domain including COVID-19. Figure 9: Technical factors in the FL ecosystem, which are leading to a variety of adversarial attacks. * _Robust solutions for statistical heterogeneity:_ In the FL, the data used in local model training can be highly diverse at each site (e.g., different languages used in next-word-prediction tasks) leading to poor convergence of the global model [64]. Since the data mostly violate the IID assumption, the accuracy of the global model minimally improves in each round. Although some strategies (data augmentation, data sharing, etc.) for addressing the non-IID problem in the FL setting have been proposed [65], this topic still needs rigorous work to eliminate all limitations stemming from all categories (i.e., attributes, labels, distributions, and temporal skews) of non-IID data. * _Robust strategies for client and server behavior analysis:_ In the FL paradigm, it is quite challenging to distinguish between benign and abnormal clients/servers because, in some cases, abnormal clients/servers can behave like real clients/servers, and vice versa. However, clients and servers are two of the most critical components of the FL paradigm, so analysis of their behavior to ensure reliable results and fairness is imperative in the FL setting. To this end, integration of the latest technologies, such as blockchain, RL, and anomaly-detection algorithms with FL, is handy for restricting manipulations of data or parameters, tampering with the training phase, and the possibility of launching collaboration attacks. In the future, it will be interesting to devise robust strategies for client/server behavior analysis to ensure the smooth operation of FL. * _Credible incentive mechanisms for good data contributors:_ In the FL setting, most clients can have a free ride and can leave the FL system at any time, leading to poor convergence of the global model. To ensure smooth mechanisms, incentive mechanisms for clients have been introduced recently in the FL paradigm. However, the selection of a pool of suitable clients that can contribute good data and stay active throughout the model training process is tricky. To this end, credible incentive mechanisms for selecting good data contributors (and performance analyses) are needed. Furthermore, devising multicriteria-based incentive mechanisms (e.g., activeness and availability of clients, data quality, resource availability, motivation) is a vibrant area of research.