## 2 Background
### An Overview of Federated Learning
#### 2.2.4 Federated Learning Systems Taxonomy
This methodology is commonly employed in situations with a reasonably equal distribution of data among participants and without substantial disparities in the quantity or significance of the data. In contrast, in imbalanced distribution, participants' data are not dispersed equally, resulting in some participants having noticeably more data than others. Managing data imbalance is a crucial factor to consider, as it might impact the FL process's performance and fairness. * Data heterogeneity: A vital component of the FLS taxonomy is data heterogeneity, which describes the variation in the kinds, forms, and quality of data among nodes or participants in an FLS [29]. The FL context offers different opportunities and problems when dealing with heterogeneous data. Homogeneous data refers to particular instances of FL where the data possessed by participants exhibits a considerable degree of similarity concerning data type, format, and quality. The utilization of homogeneous data in the FL process facilitates the training of models by enabling a more streamlined approach since the consistency of the data allows for easier training. Homogeneous situations can facilitate model aggregation, sharing updates, and making assumptions about data features. On the other hand, heterogeneous data relates to scenarios when the data obtained from diverse participants exhibit notable variations in terms of data kinds, formats, and quality. Heterogeneity can manifest in myriad ways, such as disparities in feature representations, variations in data preparation techniques, and discrepancies in data-gathering methodologies. Data heterogeneity arises for a variety of reasons, such as the utilization of disparate technologies, the involvement of many companies, and the integration of data from sources that possess separate data schemas. The issue of data heterogeneity is highly significant in the FL context, as it has notable implications for the capacity to develop a valuable global model from varied data sources while ensuring data privacy and model performance. The efficient management of data heterogeneity and adaptation to accommodate the different attributes of individual participants' data are crucial considerations in developing effective FLSs. * Data skewness: The concept of data skewness holds significant relevance within the FLS taxonomy, as it specifically refers to the uneven distribution of data across the participants or nodes in an FLS. Skewness pertains to the extent of asymmetry or lopsidedness in the distribution of data [30]. The comprehension of data skewness is essential due to its potential impact on the performance, fairness, and convergence of models in the FL context. In certain instances of FL, the distribution of data among participants may exhibit a uniform skew. This implies that each participant's data are subject to a comparable degree of skewness. Uniform skewness is observed when participants show identical patterns of data distribution despite potential variations in the quantity of data. By contrast, non-uniform skewness is observed when the skewness of the data distribution across different participants varies. Certain participants may have heavily skewed data distributions, while others may have more evenly balanced ones. Dealing with non-uniform skewness can pose difficulties as it necessitates accepting diverse levels of skewness in the data distribution. The presence of data skewness In FL gives rise to several challenges, including training imbalance, model bias, concerns over privacy, and increased communication overhead. Weightedaggregation approaches can be utilized in FLSs to address the issue of data skewness and reduce its impact. These strategies involve allocating varying weights to participants based on the degree of skewness in their data distribution. Participants who possess more highly skewed data may be assigned lower weights in order to prevent their data from exerting an excessive impact on the overall model.