The field of clustering and multi-view learning is moving towards more decentralized and collaborative approaches, with a focus on preserving privacy and handling incomplete or heterogeneous data. Researchers are exploring new algorithms and frameworks that enable devices or institutions to jointly cluster data without sharing their local datasets, or to learn from multiple views with missing or overlapping features. These developments have the potential to improve the accuracy and robustness of clustering and classification models in a variety of applications, including healthcare and computer vision. Noteworthy papers include: Federated k-Means via Generalized Total Variation Minimization, which proposes a privacy-friendly federated k-means algorithm, and Collaborative Learning of Semantic-Aware Feature Learning and Label Recovery for Multi-Label Image Recognition with Incomplete Labels, which introduces a novel collaborative learning framework for multi-label image recognition with incomplete labels.