Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation. However, EI faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EI in the eyes of stakeholders. This survey comprehensively summarizes the characteristics, architecture, technologies, and solutions of trustworthy EI. Specifically, we first emphasize the need for trustworthy EI in the context of the trend toward large models. We then provide an initial definition of trustworthy EI, explore its key characteristics and give a multi-layered architecture for trustworthy EI. Then, we summarize several important issues that hinder the achievement of trustworthy EI. Subsequently, we present enabling technologies for trustworthy EI systems and provide an in-depth literature review of the state-of-the-art solutions for realizing the trustworthiness of EI. Finally, we discuss the corresponding research challenges and open issues.

翻译：边缘智能（EI）融合了边缘计算（EC）与人工智能（AI），将AI能力扩展到网络边缘，以实现实时、高效、安全的智能决策与计算。然而，由于资源受限、异构网络环境以及不同应用的多样化服务需求，EI面临着各种挑战，这些挑战共同影响了利益相关者对EI可信性的认知。本综述全面总结了可信EI的特征、架构、技术与解决方案。具体而言，我们首先强调了在大型模型发展趋势下对可信EI的需求。随后，我们给出了可信EI的初步定义，探讨了其关键特征，并提出了可信EI的多层架构。接着，我们总结了阻碍实现可信EI的几个重要问题。之后，我们介绍了构建可信EI系统的使能技术，并对实现EI可信性的现有解决方案进行了深入的文献综述。最后，我们讨论了相应的研究挑战与开放性问题。