The field of federated learning and clustering is moving towards addressing uncertainty and heterogeneity in data. Researchers are proposing novel frameworks and methods to handle challenges such as incomplete, redundant, or corrupted data, as well as semantic conflicts and aggregation uncertainty. These innovations aim to improve the robustness and trustworthiness of federated learning and clustering models. Notable papers in this area include the Enhanced Federated Deep Multi-View Clustering framework, which achieves superior robustness against heterogeneous uncertain views, and the Semi-Supervised Federated Multi-Label Feature Selection method, which adapts fuzzy information theory to a federated setting and outperforms other approaches in non-IID data distribution settings. The Scalable Parameter-Light Spectral Method for Clustering Short Text Embeddings also shows promising results, with its estimator and Cohesion Ratio evaluation metric enabling efficient and reliable clustering of short text data. Additionally, the Structure-Aware Prototype Guided Trusted Multi-View Classification framework introduces prototypes to represent neighbor structures, facilitating more efficient and consistent discovery of cross-view consensus. Other notable works include the You Can Trust Your Clustering Model, a parameter-free self-boosting plug-in for deep clustering, and the Robust Gene Prioritization via Fast-mRMR Feature Selection, which proposes a more robust and efficient pipeline for gene prioritization in high-dimensional omics data.