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Pattern recognition plays an important role in the process of knowledge discovery. The construction of easily describable and interpretable classification rules is of vital importance in pattern recognition. In this study, we propose a development of fuzzy rule-based classifier for multiclass classification problems and elaborate on a privacy-preserving realization of the proposed methodology in the presence of decentralized datasets. Fuzzy rule-based models provide an effective and efficient alternative for characterizing the complex relationship between the input variables and target classes. An overall design process of the proposed classifier consists of two main phases: (a) formation of information granules (clusters) to reveal the underlying structure of the training data, and (b) construction of local classification rules whose outputs reflect the probability distribution of the input data over all the classes. The constructed information granules form a backbone of the architecture of the classifier while the optimization of the parameters of local rules is carried out through using a gradient descent method with the guidance of the cross-entropy loss function. Furthermore, a federated gradient-based optimization mechanism is utilized to construct fuzzy classifier in a privacy-preserving approach. The originalities of the proposed methodology are twofold: first, a design of fuzzy classifier through the synergy of cluster-centric architecture and the cross-entropy loss function is presented. Second, we augment the proposed fuzzy classifier based on the concept of federated learning such that it can learn from distributed data without sacrificing data security and confidentiality. Experiments are carried out on a two-dimensional synthetic dataset and a number of real-world datasets. Experimental results show the excellent classification capability of the proposed classifier realized in the centralized way and in the federated learning environment.

Multiview clustering has been received considerable attention due to the widespread collection of multiview data from diverse domains and sources. However, storing multiview data across multiple devices in many real scenarios poses significant challenges for efficient data analysis. Federated learning framework enables collaborative machine learning on distributed devices while preserving privacy constraints. Even though there have been intensive algorithms on multiview fuzzy clustering, federated multiview fuzzy clustering has not been adequately investigated so far. In this study, we first develop the federated learning mode into multiview fuzzy clustering and realize the federated optimization procedure, called federated multiview fuzzy C-means clustering. Then, we design an original strategy of consensus prototype learning during federated multiview fuzzy clustering. It is termed as federated multiview fuzzy C-means consensus prototypes clustering (FedMVFPC). We also further develop the federated alternative optimization algorithm with proven convergence. This study also introduces the notion of clustering prototype communication within the federated learning framework, and integrates the clustering prototypes of different views into a unified optimization formulation. The experimental studies on various benchmark datasets demonstrate that the proposed FedMVFPC method improves the federated clustering performance and efficiency. It achieves comparable or better clustering performance against the existing state-of-the-art multiview clustering algorithms.

Integrating multiresponsive molecules into a single crystal is a novel attempt in the area of photomechanical crystalline materials. In this work, a photomechanical crystal (9AC-NPE; 9AC = 9-anthracenecarboxylic acid, NPE = 4-(1-naphthylvinyl) pyridine) was prepared viacocrystallization. Remarkably, this cocrystal contained two distinct photoresponsive molecules, each corresponding to undergo a unique photodimerization reaction. Although these molecules need to satisfy appropriate topological stacking criteria, both photodimerization reactions occurred under ultraviolet irradiation, contributing to the photosalient effects, negative photochromism, and fluorescence enhancement of the cocrystal. The photosalient behavior of the cocrystal under white-light irradiation prompted us to further examine the differences between the two photodimerization reactions, particularly their reaction rate and response wavelength. Furthermore, the utility of this system in optical printing was demonstrated. Therefore, this work enhances the understanding of the design and application of photomechanical crystalline materials and can serve as an important reference for future efforts in this field.