Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the requirement for on-board processing power and minimises the data transfer time to enable real-time applications. Meanwhile, with federated learning (FL), UAVs can enhance their intelligent decision-making capabilities by learning from other UAVs without directly accessing their data. This facilitates rapid model iteration and improvement while safeguarding data privacy. However, in many UAV applications such as UAV logistics, different UAVs may perform different tasks and cover different areas, which can result in heterogeneous data and add to the problem of non-independent and identically distributed (Non-IID) data for model training. To address such a problem, we introduce a novel cloud–edge–end collaborative FL framework, which organises and combines local clients through clustering and aggregation. By employing the cosine similarity, we identified and integrated the most appropriate local model into the global model, which can effectively address the issue of Non-IID data in UAV logistics. The experimental results showed that our approach outperformed traditional FL algorithms on two real-world datasets, CIFAR-10 and MNIST.

The primary goal of the Know Your Customer (KYC) process is to deter financial crimes, such as money laundering, by ensuring that banks can accurately identify and understand their customers. This not only aids in crime prevention but also helps banks in offering tailored services and in executing effective risk management. Traditional KYC verification methods, however, tend to be complex and costly. In response to these challenges, this paper introduces an innovative KYC system designed to efficiently comply with regulatory standards, thereby minimizing the risk of non-compliance penalties. This novel system incorporates Self-Sovereign Identity (SSI) technology, granting individuals unprecedented control over their personal KYC information. We have developed and tested this system using actual user scenarios to assess its performance. The findings from our evaluation indicate that this KYC system delivers superior performance across various key metrics, showcasing its potential to transform the current KYC landscape by making the process more user-centric and cost-effective.

With the rapid advancement of intelligent technologies and network environments, the efficient and accurate handling of uncertain decision-making information has become an urgent challenge. Traditional methods often struggle to process complex and incomplete information, especially in cyber defense. To address this, we introduce discrete multi-dimensional Z-numbers (MZs) as a mathematical tool for modeling uncertainty and reliability in network defense decisions. This paper proposes a synthesis method for MZs, enabling the integration of multi-source information while considering both uncertainty and reliability. By leveraging a hidden probability model, we extend MZs into multi-dimensional Z⁺-numbers, enhancing their expressiveness in handling uncertainty. Furthermore, we define utility functions based on MZs and develop a multi-attribute group decision-making framework tailored for network defense. This approach offers a novel perspective for designing strategies against highly adaptive and covert cyberattacks. The proposed method is validated through a case study on the network security assessment of an intelligent logistics company. Results demonstrate significant improvements in the accuracy and efficiency of decision-making, highlighting the method’s advantages and broad potential in cyber defense. Beyond logistics, this integrated MZ-based decision framework provides an adaptable and intelligent tool for strengthening network security defenses.