## 2 Background
### Communication
FLSs enhance the performance and utility of models across numerous applications and user scenarios by successfully managing outliers, ensuring data quality and model reliability. Various techniques for detecting outliers, including statistical methods, clustering algorithms, and robust machine learning models, are utilized to find abnormal data points. * Statistical outlier handling methods: Statistical techniques are essential tools for addressing outliers within FLSs, offering a quantitative framework for detecting and effectively handling anomalies in data. Methods such as the Z-score, interquartile range, or Tukey's fences are commonly used to identify outliers by quantifying their deviation from the dataset's mean or median. Through the application of statistical metrics, FLSs can pinpoint data points that significantly deviate from the established norm, signifying their potential classification as outliers. Once identified, these outliers can be managed using techniques such as data imputation, transformation, or exclusion to prevent them from unduly affecting the collaborative model training process. Methods for controlling statistical outliers offer a systematic and objective approach to preserving the integrity of data utilized in FL, thereby enhancing the precision and reliability of the resulting machine learning models. * Clustering outlier handling algorithms: Clustering algorithms are efficient tools for managing outliers in FLSs, especially when dealing with diverse and heterogeneous data sources. These methods facilitate the clustering of data points that exhibit similarities, allowing the detection and analysis of patterns inherent in the data. Outliers, characterized by significant deviation from the norm, frequently show unusual clustering patterns, making their identification more straightforward. FLSs can effectively detect outlier clusters using clustering algorithms such as k-means, hierarchical clustering, or DBSCAN. Clustering techniques aid in handling outliers within FLSs, providing a data-driven and adaptable approach. This ensures the robust and accurate collaborative training of models, regardless of the diversity of data sources and patterns. Robust aggregation machine learning algorithms: Robust aggregation algorithms play an essential part in FLSs by effectively managing outliers, particularly in scenarios with noisy or inconsistent data originating from multiple sources. These algorithms are designed to minimize the impact of outliers on the aggregation process, ensuring that inaccurate or deceptive data points do not significantly distort the overall model. The use of robust aggregation strategies helps mitigate the influence of outliers during the model aggregation phase. Techniques like the trimmed mean, median-based aggregation, or methods derived from robust statistics are effective in achieving this objective. FLSs can thus maintain the integrity of the shared model, even when confronted with outliers, by reducing the significance of extreme or incorrect updates originating from individual devices. Robust aggregation algorithms are of utmost importance in enhancing the robustness of FL models. These algorithms guarantee that the resulting model accurately captures the collective intelligence of the devices involved, even in scenarios where the data are contaminated with noise or anomalies. In summary, statistical techniques offer a straightforward and comprehensible approach, albeit potentially lacking in their ability to handle intricate data distributions effectively. Clustering algorithms can uncover subtle patterns within datasets but may be sensitive to parameters and initialization. Robust aggregation methods have been purposefully developed to address the presence of outliers during the process of model aggregation in FLSs, thereby guaranteeing the creation of a more dependable and resilient global model.