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In recent years, with the increasing importance of privacy protection, many laws and regulations have standardized data usage, requiring companies to obtain user consent to access personal data. This has become more challenging for models that require large amounts of data for training. Therefore, the concept of federated learning was proposed in 2016, aiming to train models with different clients without sharing data to ensure data privacy. However, federated learning faces several challenges, including heterogeneous devices, data security, data heterogeneity, communication costs, and training time costs. This study focuses on addressing the issue of data heterogeneity, where the data distribution among participating clients differs significantly, leading to poor performance of the aggregated model after training. To tackle this problem, we propose a federated clustering cyclic algorithm, which involves two-step clustering of clients to make the data distribution of each cluster approach independent and identically distributed. We also introduce deep neural fuzzy methods to handle fuzzy, uncertain, or incomplete data. According to experimental results, the proposed deep neuro-fuzzy clustered cyclic algorithm outperforms methods such as FedAvg, FedProx, and CyclicFL on various non-IID datasets, with accuracy approaching that of centralized learning in certain experiments. This indicates that the deep neural fuzzy methods and clustering cyclic algorithm DNCC presented in this study can improve the accuracy of global models, especially in increasingly non-IID scenarios. Furthermore, we extend this method to big data processing to cope with more complex data environments.

With (EC) the rise of global economy and Electronic Commerce (EC), efficient inter-organizational planning and deployment for value chain processes have become important. Radio-frequency Identification (RFID), Near Field Communication (NFC), and related wireless technologies are evaluated to be some of the most significant technological innovations in the twenty-first century. In the past few years, wireless and context-awareness technology have led to much hope and optimism. The mainstream press hails these innovations as the avant-garde in technology and business. The Internet of Everything (IoE) goal is the intelligent connection of people, process, data, and things. The IoE describes a world where billions of objects have sensors to detect, measure, and assess their status, all connected over public or private networks using standard and proprietary protocols. Hence, this special issue investigates the state-of-art AI and deep learning approaches for successful systems or applications in the IoE environment. In addition, this special issue also wants to understand the direct and indirect effects of using these smart technologies to build language information processing based on the Web of Things (WoT) around the smart cities and societies.