  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.
