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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.
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 exponential growth of Artificial Intelligence of Things (AIoT) has resulted in an unparalleled fusion of AI with IoT technologies, giving rise to intricate systems that present vast opportunities for automation, productivity, and data-centric decision-making. Nevertheless, this amalgamation also poses substantial obstacles regarding safeguarding online information and upholding confidentiality. The chapter extensively examines the difficulties associated with these issues and the tactics employed to surmount them. The chapter commences by delineating the distinctive susceptibilities inherent in AIoT systems, with a particular emphasis on how the interconnection of AI and IoT technologies gives rise to novel avenues for data breaches and privacy infringements. It then explores the most recent approaches and technologies used to protect data sent over AIoT networks. These include improved encryption methods, secure data transfer protocols, and solutions based on blockchain technology. A substantial chunk of the chapter focuses on privacy-preserving strategies in AIoT. The text examines the equilibrium between data usefulness and privacy protection. It delves into techniques like anonymization, differential privacy, and federated learning as means to safeguard user data while ensuring the effectiveness of AIoT systems. The chapter also examines regulatory and ethical factors, thoroughly examining current and developing legislation and regulations that oversee data security and privacy in AIoT. The content incorporates case studies and real-world examples to demonstrate the pragmatic implementation of theoretical principles. Ultimately, the chapter predicts forthcoming patterns and difficulties in this swiftly progressing domain, providing valuable perspectives on possible AIoT security and privacy protocol advancements. This resource is vital for professionals, researchers, and students engaged in AIoT, cybersecurity, and data privacy. It provides them with the necessary information and tools to protect against the ever-changing threats in this dynamic field.
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