## 4 Results and Discussion
### Discussion  
This work aims to identify the techniques used to overcome the non-IID data problem and techniques that provide communication efficiency in an FL environment. To address RQ1, we examined the selected studies that aim to overcome the non-IID data challenge in their work. When evaluating their work, these studies need to select a suitable distribution to introduce the non-IID data distribution between the clients. As shown in Figure 7, most of the studies introduce the label distribution skew, specifically quantity label imbalance, by dividing the datasets between the clients according to their class, such that the clients can access only a limited number of classes. Also, the distribution label imbalance was widely addressed, where the clients had variations in their class distribution. To address the feature skew, the studies utilized the FEMNIST (Federated Extended MNIST) dataset that divides the datasets based on the writer, and in the selected studies, the writers were represented as the clients. However, we can see that most of the studies did not focus on the remaining skew types, and the label distribution skew was the widely addressed skew. To address RQ2, we thoroughly examined the retrieved studies and analyzed the techniques utilized. Figure 8 shows the most widely used techniques to overcome the non-IID data challenge in FL. Based on the selected studies and findings outlined in this work, several critical research hotspots have emerged in FL. One of the prominent areas of interest is the development of novel aggregation schemes to address the non-IID data challenge used in 18% (17 studies) of the selected studies. These schemes aim to improve model accuracy and convergence by effectively aggregating local updates from heterogeneous client devices. Additionally, there is growing attention towards exploring clustering in FL, which is used in 16% (15 studies) of the studies. Clustering is a technique used to group clients into clusters to overcome the non-IID data challenge, using different clustering criteria such as model similarity and the client's dataset size. Figure 19: Heatmap between the most commonly used learning models and the respective datasets utilized in the studies that provide solutions to both challenges. Figure 18: Percentage of the top commonly used datasets in the studies that provide solutions to both challenges. Aggregation in FL: As the first study that proposed FL also highlighted the importance of the non-IID data problem and suggested an aggregation method that is a weighted averaging scheme (FedAvg) based on clients' dataset size to overcome this challenge, many studies were inspired by this work and tried to enhance it. For instance, the work in [50] delayed the aggregation process by sending the model back to some clients for further training to enhance the model, while the study in [105] enhanced the calculation of the weights to be based on indices of statistical heterogeneity instead of just the client dataset size. While the study in [131] adds to the FedAvg a regularization to lower the excess risk, the study in [133] also uses regularization in its aggregation scheme to penalize the diverging model. Clustering in FL: Clustering is also used to overcome the non-IID data challenge, where the server clusters clients based on certain criteria. In [54, 88], the clients are clustered based on the similarity of their models, whereas in [97], the central server clusters clients based on their dataset size. The work in [100] mandates every client to report specific statistics about their local dataset in order to perform clustering. Aggregation and clustering are the two widely used techniques that aim to overcome non-IID data on the server side by utilizing information extracted from the local model. However, these techniques raise security concerns as they involve analyzing the obtained local model and sharing information.