The research landscape in federated learning (FL) is witnessing significant advancements aimed at enhancing privacy, security, and model robustness. A notable trend is the development of frameworks that leverage distributed learning to maintain user privacy while improving model performance. These frameworks often integrate innovative regularization techniques and similarity-based feature engineering to handle heterogeneous data challenges inherent in federated settings. Additionally, there is a growing focus on mitigating the effects of covariate shift through novel pruning and regularization methods, ensuring more robust model aggregation across diverse data distributions. The field is also advancing in the application of FL to risk-based authentication and malicious user prediction, demonstrating improved accuracy and scalability in real-world scenarios. Notably, the integration of federated learning with risk-based authentication introduces a new paradigm for privacy-focused security in distributed environments. In malicious user prediction, federated learning models show significant improvements in key performance indicators, highlighting their potential in enhancing data security in cloud environments. Overall, the current developments in FL are pushing the boundaries of privacy-preserving machine learning, with a strong emphasis on practical applications and robustness against data distribution variations.